KR20160124079A - Systems and methods for in-memory database search - Google Patents

Abstract

A system and method for identifying related entities using an entity co-occurrence knowledge base is disclosed. Embodiments extract entities identified in search queries using the entity concurrent knowledge base of entities extracted from the entity index corpus, and provide search results as related entities. Embodiments for generating search suggestions using fuzzy-score matching with an entity concurrent knowledge base are disclosed. Embodiments extract partial entities from search queries, execute a matching algorithm based on the type of extracted entities, and perform searches on an entity concurrent knowledge base. Also disclosed are embodiments for generating search suggestions of related entities based on coincidence and / or fuzzy-score matching. Embodiments process partial search queries and provide suggested queries of completed queries used as new search queries. In addition, an embodiment is disclosed in which a search query is generated using co-occurrence of entities by extracting entities from search queries using entities and trend co-occurrence databases. Also disclosed are embodiments that enable geographic and location-based search functionality in a content management system.

Description

[0001] SYSTEM AND METHODS FOR IN-MEMORY DATABASE SEARCH [0002]

This disclosure relates generally to methods and systems for information retrieval, and more particularly to methods for searching for related entities using entity co-occurrences. This disclosure relates generally to query enhancement and more particularly to query enhancements using fuzzy-score matching in entities and concurrency of entities, ). BACKGROUND I. Field [0002] The present disclosure relates generally to computer-based query processing, and more particularly to electronic search suggestions of related entities based on concurrency and / or fuzzy score matching. This disclosure relates generally to methods and systems for information retrieval, and more particularly to methods for obtaining search suggestions. This disclosure relates generally to search engines and content management and, more particularly, to an extension of search engine technology in content management systems to enable geotagging and named entities enrichment of digital content. .

In a commercial context, a well-known search engine parses a set of search terms and returns a list of items sorted in some way (web pages in a typical search) do. Most known methods for performing searches build a search query database that can be used to generate indexes based on keywords, typically based on historical references of other users . User search queries may include one or more entities identified by a name or attribute that may be associated with the entity. Entities may include organizations, people, location, date and / or time. In a typical search, if the user is searching for information related to two particular organizations, the search engine may return various results that may be about mixed entities with the same name or similar names. The latter approach allows the user to find a large amount of documents that may not be relevant to what the user is actually interested in.

Thus, there is a need for a way to search for relevant entities, which allows the user to find relevant entities of interest.

The user frequently uses a search engine for locating the information of interest on the Internet or any database system. The search engine typically operates by receiving a search query from a user and returning a search result to the user. The search results are typically ordered by the search engine based on the relevance of each returned search result to the search query. Therefore, the quality of the search query is very important for the quality of the search result. However, in most cases, search queries from the user can be written incompletely or partially (e.g., the search query does not include enough words to generate a set of focused related results, (For example, Bill Smith's spelling may be mislabeled as Bill Smitth). In some cases, there may be a number of irrelevant results.

One common way to improve the quality of search results is to enhance search queries. One way to enhance search queries is to generate a possible presentation term based on user input. To this end, several approaches propose methods for identifying candidate query refinements for a given query from past queries presented by one or more users. However, these schemes are sometimes based on query logs that can lead users to an uninteresting result. There are other ways to use other technologies that may not be accurate enough. Thus, there is still a need to improve or enhance search queries from users to obtain more accurate results,

A user frequently uses a search engine for locating information of interest from the Internet or any database system. The search engine typically operates by receiving a search query from a user and returning a search result to the user. The search results are typically arranged based on the relevance of each returned search result to the search query. Therefore, the quality of the search query is very important for the quality of the search result. However, in most cases, search queries from the user can be written incompletely or partially (e.g., the search query does not include enough words to generate a set of focused related results, (Which, for example, Bill Smith's spelling can be mistakenly written as Bill Smitth).

One common way to improve the quality of search results is to enhance search queries. One way to enhance search queries is to generate a possible presentation term based on user input. To this end, several approaches propose methods for identifying candidate query refinements for a given query from past queries presented by one or more users. However, these schemes are sometimes based on query logs that can lead users to an uninteresting result. There are other ways to use other technologies that may not be accurate enough. Thus, there is still a need to improve or enhance search queries from users to obtain more accurate results, and also to provide users with relevant interested entities as they type in search queries

The search engines include a number of features to provide a forecast of the user's query. Such prediction may include query auto-complete and search suggestions. Today, such prediction methods are based on historical keyword reference data. Such historical reference data may not be accurate because one keyword may be for a number of topics in a single text.

In addition, user search queries may include one or more entities identified by names or attributes that may be associated with the entity. Entities may include organizations, people, locations, events, dates and / or times. In a typical search, if the user is searching for information related to two particular organizations, the search engine may return various results that may be about mixed entities with the same name or similar names. The latter approach allows the user to find a large amount of documents that may not be relevant to what the user is actually interested in.

Therefore, there is a need for a method for acquiring a faster and more accurate search word.

Content management and document management systems for document versioning and collaboration project management are known. One non-limiting example may be the application suite of Microsoft's Sharepoint 2013® software and tools. Microsoft's Sharepoint 2013® is a software suite developed by Microsoft for collaboration, file sharing, and web publishing. Sharepoint 2013® can provide users with vast amounts of content or information, and it is difficult for users to find the most relevant information for a particular situation. To alleviate these issues, Sharepoint 2013® provides a search engine to assist users in discovering the content they need. Users can enter keyword-based navigation queries, and the search engine in Sharepoint 2013® can return to the user a list of the most relevant results found in the context of the Sharepoint 2013® platform once the content has been indexed .

Occasionally, a user may want to discover content related to geographic entities in Sharepoint 2013® or other types of entities such as named organizations or people in documents. Sharepoint 2013® does not provide immediate functionality to automatically extract entities from a document. In particular, it does not support geotagging content for extracting geographic entities and returning them to a geographic location. In addition, Sharepoint 2013® does not support entity tagging to identify clearly and correctly named entities, such as organizations or people within a document. However, the Sharepoint 2013® search can be extended to enable effective geographic searches and other entity-related searches involving entity-based search facets. Earlier versions of Sharepoint 2013® included "FAST Search" for SharePoint, from which a content processing pipeline could be extended with a sandboxed application, which was slow and accessible There was a limit on the information available.

Sharepoint 2013® introduces a much more open API that allows you to add professional linguistics such as concept extraction, association extraction, geotagging, summarization, and sophisticated text analysis. Thus, there is an opportunity to extend the functionality of the Sharepoint 2013® search engine to enable geographic and other entity-based exploration.

A method for searching for related entities using entity coincidence is disclosed. In one aspect of the present disclosure, the method may be employed in a search system that may include a client / server. In one embodiment, the search system includes a user interface for a search engine in communicating with one or more server devices via a network connection. The server device may include an entity indexed corpus of electronic data, an entity co-occurrence knowledge base database, and an entity extraction computer module. The knowledge base may be constructed as an in-memory database and may include other components such as one or more search controllers, a plurality of search nodes, a collection of compressed data, and a solvency module. One search controller may be selectively associated with one or more search nodes. Each search node can independently perform a fuzzy key search through a collection of compressed data and return a set of scored results to its associated search controller.

In one embodiment, a computer-implemented method includes receiving an search query from an entity extraction computer having one or more entities from a client computer; The entity extraction computer compares each entity with one or more occurrences of each entity in the co-occurrence database; An entity extraction computer extracts a subset of one or more entities from a search query, said extraction comprising a subset of one or more entities based on the level of certainty of co-occurrence of one or more related entities in an electronic data corpus, In response to determining that each entity of the concurrent database exceeds the trust score of the co-occurrence database; An entity extraction computer assigns an index identifier (index ID) to each of the entities within the plurality of extracted entities; Storing an index ID for each of a plurality of extracted entities in an electronic data corpus indexed by an index ID corresponding to each of one or more related entities; A search server computer is configured to search for an entity indexed electronic data corpus to identify the index IDs of data records in which at least two of the plurality of extracted entities are co- ≪ / RTI > And the search server computer comprises constructing a search result list having a data record corresponding to the identified index IDs.

In one embodiment, the system comprises one or more server computers with one or more processor-executable computer-readable instructions for a plurality of computer modules, the plurality of computer modules being configured to receive user input of search query parameters A configured entity extraction module; And a search server module, wherein the entity extraction module extracts a plurality of entities from the search query parameters, the extraction comprising: extracting entities and one or more related entities in the electronic data corpus, By comparing an entity concurrency database containing a trust score indicative of the entities in the plurality of extracted entities with each entity in the plurality of extracted entities; Assigning an index identifier (index ID) to each entity in the plurality of extracted entities; Wherein the search server module is configured to store an index ID for each of a plurality of extracted entities in an electronic data corpus indexed by an index ID corresponding to each of one or more associated entities, At least two of the plurality of extracted entities are configured to search for an entity index electronic data corpus to identify index IDs of concurrently occurring data records; And construct a search result list having a data record corresponding to the identified index ID.

In another embodiment, the non-transitory computer readable medium has computer executable instructions stored thereon, the computer executable instructions causing the entity extraction computer to receive user input of search query parameters; Wherein the entity extraction computer causes a plurality of entities to be extracted from the search query parameters, the extraction including a confidence score indicating a concurrency level of concurrency of the extracted entity and one or more related entities in the electronic data corpus To be performed by comparing the entity concurrency database with each entity in the plurality of extracted entities; The entity extraction computer to assign an index identifier (index ID) to each entity in the plurality of extracted entities; The entity extraction computer storing an index ID for each of a plurality of extracted entities in an electronic data corpus indexed by an index ID corresponding to each of one or more related entities; The search server computer causes the entity index electronic data corpus to be searched to locate a plurality of extracted entities and to identify an index ID of a concurrently occurring data record of at least two of the plurality of extracted entities; And causing the search server computer to construct a search result list having a data record corresponding to the identified index ID.

A method for generating a search query using fuzzy-score matching and simultaneous occurrence of entities in a knowledge base is disclosed. In one aspect of the present disclosure, the method may be employed in a search system that may include a client / server type architecture. In one embodiment, the search system may include a user interface to a search engine that communicates with one or more server devices via a network connection. The server device may include an entity extraction computer module, a fuzzy-score matching computer module, and an entity concurrent knowledge base database. The knowledge base may be constructed as an in-memory database and may include other hardware and / or software components such as one or more search controllers, a plurality of search nodes, a collection of compressed data, and a deconvolution computer module . One search controller may be selectively associated with one or more search nodes. Each search node can independently perform a fuzzy key search through a collection of compressed data and return a set of scored results to the associated search controller.

In another aspect of the present disclosure, the method includes an entity extraction module capable of performing a partial entity extraction from a search query provided to identify whether the search query refers to an entity and if so, what type of entity it refers to . The method may also include a fuzzy-score matching module that generates algorithms based on the type of extracted entity and performs a search on the entity concurrent knowledge base. Additionally, lookup text portions that are not detected as corresponding to the entities are treated as conceptual features such as topics, facts, and key phrases that may be employed to search for an entity coincidence knowledge base. In an embodiment, an entity co-occurrence knowledge base is a repository of entities that can be indexed as entities to entities, entities to topics, or entities to facts, among others. repository) is included, and the search query is completed by returning a quick and accurate presentation word to the user.

In one embodiment, a method is disclosed. The method includes: an entity extraction computer receiving user input of search query parameters from a user interface; Entity extraction computer compares search query parameters with an entity concurrency database having instances of concurrent occurrence of one or more entities in an electronic data corpus and determines at least one Extracting one or more entities from the search query parameters by identifying an entity type of the search query parameters; Selecting a fuzzy matching algorithm that searches the entity concurrent database for a fuzzy-score matching computer to identify one or more records associated with search query parameters, wherein the fuzzy matching algorithm corresponds to at least one identified entity type do. The method includes the steps of: a fuzzy-score matching computer searching the entity concurrency database using a selected fuzzy matching algorithm and forming one or more suggested search query parameters from one or more records based on the search; And providing the one or more suggested search query parameters via the user interface.

In another embodiment, a system is provided. The system is configured to receive user input of search query parameters from a user interface and compare search search parameters with an entity concurrency database having instances of concurrent occurrence of one or more entities in an electronic data corpus, An entity extraction module configured to extract one or more entities from the search query parameters by identifying at least one entity type corresponding to one or more entities in the parameters of the plurality of computer modules, And one or more server computers having executable computer readable instructions. The system further includes a fuzzy-match matching module configured to select a fuzzy matching algorithm to search for an entity co-occurrence database to identify one or more records associated with search query parameters, wherein the fuzzy matching algorithm comprises at least one identified Corresponds to the entity type. The fuzzy-score matching module searches the entity concurrency database using a selected fuzzy matching algorithm, forms one or more suggested search query parameters from one or more records based on the search, Lt; RTI ID = 0.0 > parameters. ≪ / RTI >

A method for generating a search suggestion of related entities based on coincidence and / or fuzzy score matching is disclosed. In one aspect of the present disclosure, the method may be employed in a computer search system that may include a client / server type architecture. In one embodiment, the search system includes a user interface to a search engine that communicates with one or more server devices via a network connection. The server device may include one or more processor execution instructions for a plurality of dedicated computer modules, including an entity extraction computer module, a fuzzy-score matching computer module, and an entity concurrent knowledge base database. The knowledge base may be constructed as an in-memory database and may include other components such as one or more search controllers, a plurality of search nodes, a collection of compressed data, and a deconvolution computer module. One search controller may be selectively associated with one or more search nodes. Each search node can independently perform a fuzzy key search through a collection of compressed data and return a set of scored results to the associated search controller.

In another aspect of the present disclosure, the method may include performing an entity extraction module to identify if the search query refers to an entity, and if so, a partial entity extraction from the provided search query to determine the entity type. The method may also include the fuzzy-score matching module generating algorithms corresponding to the type of the extracted entity and performing a search for the entity concurrent knowledge base. Additionally, lookup text portions that are not detected as entities are treated as conceptual features such as topics, facts, and key phrases that can be employed to search for an entity coincidence knowledge base. Among other things, an entity concurrent knowledge base that already has an repository that can be indexed as entities to entities, entities to topics, or entities to facts , And the search query is completed by returning a quick and accurate presentation word to the user.

In a further aspect of the present disclosure, the completed search query may be used for a new search query. The search system processes the new search query, performs entity extraction, discovers the associated entities with the highest score from the entity concurrent knowledge base, and provides the relevant entities with a drop down list that is useful to the user .

In one embodiment, a method is disclosed. The method comprising: an entity extraction computer receiving user input of partial search query parameters - partial search query parameters having at least one incomplete search query parameter from the user interface; Entity extraction computer compares the partial search query parameters with an entity concurrency database having instances of concurrent occurrence of one or more first entities in the electronic data corpus and determines whether the partial search query parameters include one or more first entities Extracting one or more first entities from the partial search query parameters by identifying at least one entity type corresponding to the at least one entity type; Selecting a fuzzy matching algorithm that searches for an entity coincidence database to identify one or more records associated with the partial search query parameters, wherein the fuzzy matching algorithm is adapted to search for at least one identified entity type Respectively. The method includes the steps of the fuzzy-score matching computer searching the entity concurrency database using the selected fuzzy matching algorithm, forming one or more of the presented first search query parameters from the one or more records based on the search, Wherein the matching computer provides one or more of the presented first search query parameters via a user interface and the entity extraction computer receives a user selection of one or more of the presented first search query parameters to form the completed search query parameters, The computer further includes extracting one or more second entities from the completed search query parameters. The method includes the steps of the entity extraction computer searching the entity coincidence database to form one or more presented second search lookup parameters by identifying one or more entities associated with the one or more second entities, And providing one or more of the presented second search query parameters.

In another embodiment, a system is disclosed. The system is configured to receive user input of partial search query parameters - partial search query parameters from the user interface having at least one incomplete search query parameter, and to perform partial search query parameters on one or more 1 Entities from the partial search query parameters by comparing them with an entity concurrency database having instances of concurrency of one entity and identifying at least one entity type corresponding to one or more first entities in the partial search query parameters And one or more server computers having one or more processor executable computer readable instructions for the plurality of computer modules, the entity extraction module being further configured to extract one or more first entities. The system further includes a fuzzy-match matching module configured to select a fuzzy matching algorithm that searches for an entity co-occurrence database to identify one or more records associated with the partial search query parameters, wherein the fuzzy matching algorithm includes at least one identification Lt; / RTI > entity type. The fuzzy-score matching module may search the entity concurrency database using the selected fuzzy matching algorithm, form one or more of the presented first search query parameters from the one or more records based on the search, And is further configured to provide first search query parameters. In addition, the entity extraction module may receive the user selection of one or more of the presented first search query parameters to form the completed search query parameters, extract one or more second entities from the completed search query parameters, To form one or more of the presented second search query parameters by identifying one or more entities associated with the one or more second entities and to provide one or more of the presented second search query parameters via the user interface.

 A method for acquiring search suggestions associated with entities using entities and feature co-occurrence is disclosed. In one aspect of the present disclosure, the method may be employed in a search system that includes a client / server type architecture.

The search system utilizes a method capable of employing entities stored in one or more servers, allowing for an entity database and a trends database. Entities on such a database may have a score that is indexed based on a higher score. The method of acquiring the search presenter may combine information stored in two databases for generating a single list of search presuppositions. The trend database may provide a prior search query from one or more users on the local network and / or the Internet. The entity database may provide a search suggestion based on entity extraction from a plurality of data available on the local network and / or the Internet. Such a list can provide the user with a group of more accurate and quick presentation words.

In one embodiment, a computer-implemented method comprises: receiving from a search engine a search query comprising one or more data strings, each entity corresponding to a subset of one or more strings; The computer identifying one or more entities in the one or more data strings based on comparing the entity database and the trend database with one or more entities; Identify one or more characteristics in one or more data strings that the computer is not identified as corresponding to at least one entity; The computer assigning each of the one or more features to at least one of the one or more entities based on a matching algorithm; The computer assigns an extraction score to each entity based on a score assigned to each feature assigned to each entity; The computer receiving from the entity database a first search list comprising one or more entities with a score within a threshold distance from each entity's extraction score; The computer receiving a second search list from the trend database, the second search list including one or more entities having a score within a threshold distance from an extraction score of each entity; Wherein the computer generates an aggregated list having a first search list and a second search list, and entities of the aggregation list are each ranked according to the aggregated scores; Wherein the computer comprises providing a suggested search according to the collection list.

Systems and methods are disclosed herein that enable geographic entity-based discovery in a content management system such as SharePoint 2013® from Microsoft. Embodiments are described. The method involves extending the SharePoint 2013® search architecture by adding a geographic tagging web service. The system includes a computer processor operatively associated with a computer memory and one or more I / O devices, the processor and memory configured to operate one or more SharePoint 2013 processes. The system includes a computer memory and another computer processor operatively associated with one or more I / O devices, the processor and memory hosting and providing processing for a geotagging web service, . The SharePoint 2013® system may include a crawling component, a content processing component, and a search indexing component to enable navigation of the content. The content processing component in SharePoint 2013® search can extend its functionality using the Content Enrichment Web Service (CEWS) feature.

The method involves crawling content from other sources to obtain an array of crawled properties to be transmitted for content processing. During content processing, the trigger condition determines whether the crawled properties can benefit from additional processing to supplement the original content with additional geographic metadata properties. If the crawled properties do not benefit from additional processing, the crawled properties are mapped to managed processing and sent to the search index. When the crawled properties benefit from external web service processing, the CEWS makes a simple object access protocol (SOAP) request to the configurable endpoint using HTTP (hypertext transfer protocol) or any other web service invocation method. The entity enhancement service determines the content type. If the content is in an image format, its metadata, such as a file location, is transmitted in optical character recognition (OCR), the original document is retrieved and processed asynchronously, converted to text, and sent back to the crawl component, And can be re-crawled. If the content is in text format, the geotagging web service identifies the geographic metadata and associates it with the content as management features. If the content has been geotagged, it is sent to the indexing component.

(UI) by modifying the standard layout of SharePoint 2013® navigation with standard Web development tools such as HTML, HTML 5, JavaScript, and CSS, using SharePoint 2013® Web parts, among others. Can be added. The navigation UI may assist the user in displaying geographic search results or performing geographic search queries using digital geographic features such as, for example and without limitation, a digital map. The search UI may be enhanced to perform faceted searches using additional reinforced entities or metadata associated with them.

Various other aspects, features, and advantages of the present disclosure will become apparent from the following detailed description.

The present disclosure may be better understood by reference to the following drawings. The components in the figures are not necessarily drawn to scale, but are illustrated with emphasis on the principles of the present disclosure. Reference numerals in the drawings denote corresponding parts throughout the other drawings.
1 is a block diagram illustrating an exemplary environment of a computer system in which one embodiment of the present disclosure operates.
2 is a flow diagram illustrating a method for searching using co-occurrence of entities, according to an embodiment.
3 is a flow diagram illustrating an embodiment of a simple search in which the search results returned by the system include interested entities associated therewith.
4 is a block diagram illustrating an exemplary system environment in which one embodiment of the present disclosure operates.
5 is a flow diagram illustrating a method for a fuzzy-score matching in a knowledge base and a search suggestion using entity co-occurrence, according to an embodiment.
FIG. 6 is a diagram illustrating an example of a user interface through which a search suggestion word can be generated using fuzzy matching and entity co-occurrence in the knowledge base of FIGS. 4 to 6. FIG.
7 is a block diagram illustrating an exemplary system environment in which one embodiment of the present disclosure operates.
8 is a flow diagram illustrating a method for generating search suggestions of related entities based on concurrent and / or fuzzy score matching, in accordance with an embodiment.
9 is a diagram illustrating an exemplary embodiment of a user interface associated with the method shown in FIG.
10 is a block diagram illustrating a method for obtaining a search suggestion based on entities and trend databases.
11 is a block diagram illustrating a method of obtaining a search suggestion based on an entity and a trend database by generating a list of suggestions based on an individual score of the search suggestion in each of the entity and trend databases.
12 is a block diagram illustrating a method for obtaining a search query based on entities and trend databases by generating a list of search terms based on an overall score of search queries on entities and trend databases.
13 is a diagram illustrating a system architecture for tagging content and enhancing entities in a content management system.
Figure 14 is a diagram illustrating a process for tagging and indexing content for named and geographic entity searches.

Justice

As used herein, the following terms may have the following definitions.

"Entity extraction" refers to an information processing method that extracts information such as name, location, and organization.

A "corpus" refers to a collection of one or more documents.

"Features" refers to any information derived at least in part from a document.

An " event concept store "refers to a database of event template models.

"Event" refers to one or more features that characterize the occurrence of a feature at least in real time.

An "event model" refers to a collection of data that can be used to compare and identify particular types of events.

"Module" refers to computer or software components suitable for executing at least one task.

"Feature attribute" refers to metadata associated with features, such as the location of a feature in a document, trust score, among others.

"Fact" refers to an objective relationship between features.

"Entity knowledge base" refers to a computer database containing features / entities.

A "query" refers to a computer generated request to retrieve information from one or more suitable databases.

A "topic" refers to a set of thematic information derived at least in part from the corpus.

"Geotagging" refers to the process of extracting geographic entities from an unstructured text file, which removes the ambiguity of the entities for a particular geographic location and provides geographic metadata such as geographic coordinates, geographic feature types, And attaching the data.

"Entity tagging" refers to the process of extracting named entities from unstructured text, including resolution of entity entities, entity name normalization, and attachment of entity metadata.

A "named entity" refers to a person, organization, or topic.

A "geographic entity" refers to a geographic location or geographic location.

"Crawled properties" refers to content management system metadata obtained by examining documents during crawls.

Reference will now be made in detail to the preferred embodiments, examples of which are illustrated in the accompanying drawings. The embodiments described herein are illustrative. Those skilled in the art will recognize that a number of alternative components and embodiments may be substituted for the specific examples described herein and are within the scope of the present invention. Other embodiments may be utilized and / or other changes may be made without departing from the spirit or scope of the disclosure. The exemplary embodiments described in the Detailed Description are not intended to limit the subject matter provided herein.

However, it will be understood that they are not intended to limit the scope of the invention. Modifications and further modifications of the inventive features described herein, which can be made by those skilled in the art of the present disclosure, and additional applications of the principles of the invention described herein should be considered within the scope of the present invention.

The present disclosure describes a system and method for detecting, extracting, and authenticating events from multiple sources. The sources may include a news source, a social media website, and / or any source that may contain data related to the event.

Various embodiments of the systems and methods disclosed herein collect data from other sources to identify unique events.

FIG. 1 shows a block diagram of a search system 100 in accordance with the present disclosure. The search system 100 may include a search system 104 that may include a graphical user interface 102 that accesses a search engine 104 that communicates search requests in the form of binary data to a server device 106 over a network 108 100 and one or more client computing devices having processor execution software modules associated therewith. In an exemplary embodiment, the search system 100 may be implemented in a client-server computing architecture. However, the search system 100 may include other computer architectures (e.g., a stand alone computer, a mainframe system with terminals, an application service provider (ASP) model, a peer-to-peer model, etc. ). ≪ / RTI > The network 108 may comprise any suitable hardware and software modules capable of communicating digital data between computing devices, such as a local area network (LAN), a wide area network (WAN), the Internet, a wireless network, . In that case, it should be appreciated that the system 100 may be implemented via a single network 108, or may be implemented using multiple networks 108.

The user's computing device 102 may access the search engine 104, which includes a software module capable of transmitting search queries. The search queries are parameters provided to the search engine 104, which represent the desired information for searching. The search queries are provided by the user or other software application in any suitable data format (e.g., integer, string, complex objects) compatible with the parsing and processing routines of the search engine 104 . In some embodiments, the search engine 104 is accessible through a user's computing device 102 browser or other software application, and allows a user or software application to locate information on the World Wide Web It can be a web-based tool. In some embodiments, the search engine 104 may be application software modules specific to the system 100 that enable a user or application to locate information within the database of the system 100. [

The server device 106 can be implemented in a distributed architecture across multiple server computers or as a single server device 106 and includes an entity extraction module 110, an entity concurrent knowledge base 112 and an entity index corpus 114). The entity extraction module 110 may be computer software and / or hardware modules capable of extracting proprietary entities from a given query set, such as a query string, structured data, etc., and resolving the ambiguities. Entities may include, for example, people, organizations, geographic locations, dates and / or times. During extraction, one or more feature recognition and extraction algorithms may be employed. Also, for each extracted feature, a score may be assigned that indicates that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of feature attributes, a relative weight or relevance of each feature can be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.

According to various embodiments, the entity concurrent knowledge base 112 is constructed as an in-memory computer database (not shown), but is not limited to, one or more search controllers, a plurality of search nodes, And other components (not shown), such as a collection and hygrometer computer module. One search controller may optionally be associated with one or more search nodes. Each search node can independently perform a fuzzy key search through a collection of compressed data and return a set of scored results to its associated search controller.

The entity co-occurrence knowledge base 112 may include related entities based on features and ranked by a trust score. The features that make use of a weighted model that determines which entity types are most important and which have a larger weight and determine how much reliably the extraction of the correct features is performed based on the confidence score, May be employed. Entity index corpus 114 may include data from a number of sources, such as the Internet with a massive corpus or live corpus.

2 is a flow diagram illustrating a method for searching for related entities using entity co-occurrence that may be implemented in the search system 100, such as that shown in FIG. According to various embodiments, prior to the start of the method 200, the entity index corpus 114, similar to that shown in Figure 1, may be stored in an entity (e.g., Internet, Web site, blog, word processing file, plaintext file Such as a large corpus or live corpus of electronic data. The entity index corpus 114 may include a number of indexed entities that can be updated regularly as new data is discovered.

In one embodiment, at step 202, when a user or a software application of computing device 102 provides one or more search queries that include one or more entities to search engine 104, method 200 begins. The search queries provided in step 202 are processed by the search system 100 from 1 to n each time. For example, the search query at step 202 may be a combination of keywords, such as a string, structured data, or other suitable data format. In this exemplary embodiment of FIG. 2, the keywords in the search query may be entities representing a person, organization, geographic location, date and / or time.

Search queries from step 202 may be processed for entity extraction in step 204. At this stage, the entity extraction module 110 processes the search queries from step 202 as entities and compares them all with the entity concurrency knowledge base 112 to extract and resolve as many entities as possible. During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of feature attributes, a relative weight or relevance of each feature can be determined. Additionally, the relevance of associations between features can be determined using a weighted scoring model.

In addition, features that make use of a weighted model to determine which entity types are most important and which have a larger weight, and how much reliably the extraction of correct features is performed based on the confidence score Several methods of link access may be employed. Once the entities are extracted and ranked based on the trust score, an index ID, which in some cases may be a number, may be assigned to the extracted entity in step 206.

Next, at step 208, a search based on the assigned entity index ID at step 206 may be performed. In the search stage 208, the extracted entities may be placed in the entity index corpus 114 data using standard indexing methods. Once the extracted entities are deployed, proceed to entity association step 210. At entity association step 201, all data (such as documents, video, photographs, files, etc.) (at least two extracted entities overlap) is ejected from the entity index corpus 114. Finally, at step 212, a list of potential results is constructed, sorted by relevance, and provided to the user as a search result. The list of results only shows a link to the data that the user may find relevant entities of interest.

FIG. 3 is a specific illustration of a method 300 for searching for related entities using entity co-occurrence as described above in connection with FIG. 2, prior to the start of the method 300, an entity index corpus 114 similar to that shown in FIG. 1 may be obtained from a number of sources, such as a large corpus or live corpus (Internet) Data may be fed. The entity index corpus 114 may include a number of indexed entities that can be updated as new data is discovered.

In this exemplary embodiment, the user can retrieve information about "jobs" in company "Apple. &Quot; To that end, a user may interact with one or more entities (e.g., through step 302 (e.g., via step 302) via user interface 102, which may be, Search query in the search engine). By way of example, and not limitation, a user may enter a combination of entities such as "Apple + Jobs ". Next, the search engine 104 generates search queries at step 302 and sends these queries to the server device 106 to be processed. At server device 106, entity extraction module 110 may execute an entity extraction step 304 from the search query input at step 302.

Entity extraction module 110 processes the search queries that were entered at step 302, e.g., "Apple" and "Jobs" as entities, and compares them all with the entity concurrent knowledge base 112, And eliminates their ambiguity. During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of the feature attribute, the relative weight and relevance of each of the features can be determined. Association relationships between features can be determined using a weighted scoring model.

 In addition, features that make use of a weighted model to determine which entity types are most important and which have a larger weight, and how much reliably the extraction of correct features is performed based on the confidence score Several methods of link access may be employed. As a result, a table 306 containing entities and concurrency can be created. Table 306 shows the entity "apple" and its concurrency (in this case, it could be Apple and Jobs, Apple and Steve Jobs). The table 306 may include Apple and the organization A found to be relevant because Organization A is trading with Apple and is creating "jobs" in Organization A. Other less important concurrent occurrences may be found. In that case, Apple and Jobs can have the highest score (1), so they can be listed first, then Apple and Steve Jobs can have the second highest score (0.8), and finally Apple Other organization A can be listed in the bottom with the lowest score (0.3).

Once the entities are extracted and ranked based on the trust score, the index ID, which in some cases may be a number, may be assigned to the extracted entity in step 308. [ Table 310 shows the index IDs assigned to the extracted entities. Table 310 then shows the "Apple" with index ID 1, "Jobs" with index ID 2, "Steve Jobs" with index ID 3, and "Organization A" with index ID 4 .

Next, a search step 312 may be performed based on the entity index ID at step 308. [ In the search step 312, extracted entities such as "Apple", "Jobs", "Steve Jobs", and "Organization A" may be placed in the entity index corpus 114 data using standard indexing methods.

After placing the extracted entities in the entity index corpus 114, an entity association step 314 is performed. In entity association step 314, all data (at least two extracted entities overlap), such as documents, video, photographs, files, etc., is ejected from the entity index corpus 114 to build up a list of links as search results Step 318). By way of example, and not limitation, table 316 shows how entities extracted from entity index corpus 114 are associated with data. In table 316, documents 1, 4, 5, 7, 8, and 10 show an overlap of two extracted entities, and thus links to these documents appear as search results at step 318.

4 is a block diagram of a search computer system 400 in accordance with the present disclosure. The search system 400 may include one or more user interfaces 402 for the search engine 404 in communication with the server device 406 via the network 408. [ In this embodiment, the search system 400 may be implemented in one or more dedicated computer and computer modules referenced below, including through a client / server type architecture. However, the search system 400 may be implemented using other computer architectures (e.g., a stand-alone computer, a mainframe system with terminals, an ASP model, a peer-to-peer model, etc.) . In an embodiment, the search computer system 400 may include a number of networks, such as a local area network (LAN), a wide area network (WAN), the Internet, a wireless network,

The search engine 404 may include a user interface, such as a web-based tool, that allows a user to locate information on the World Wide Web. The search engine 404 may include a user interface tool that allows a user to locate information in the internal database system. The server device 406, which may be implemented in a distributed architecture that is implemented in a single server device 406 or spans multiple server computers, includes an entity extraction module 410, a fuzzy-score matching module 412, Base database 414.

Entity extraction module 410 may include hardware and / or software that is configured to extract on-the-fly independent entities from a given set of queries, such as query strings, partial queries, structured data, Module. For example, the entities may include a person, an organization, a geographic location, a date and / or a time. During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of the feature attribute, the relative weight and relevance of each of the features can be determined. Additionally, the relevance of association between features may be determined using a weighted scoring model.

The fuzzy-score matching module 412 may include a number of algorithms that may be selected according to the entity type extracted from a given search query. The function of the algorithm is to determine whether a given search query received via a user input and the other searched strings identified by the algorithm are similar to each other or properly matched to a given pattern string. Fuzzy matching is known as fuzzy string matching, inexact matching, and approximate matching. Entity extraction module 410 and fuzzy score matching module 412 work in conjunction with entity concurrency knowledge base 414 to generate search suggestions for users.

According to various embodiments, the entity concurrency knowledge base 414 may be constructed as an in-memory database and may include components such as one or more search controllers, a plurality of search nodes, a collection of compressed data, But are not limited to, One search controller may optionally be associated with one or more search nodes. Each search node can independently perform a fuzzy key search through a collection of compressed data and return a set of scored results to its associated search controller.

The entity concurrency knowledge base 414 may include related entities based on features and ranked by a trust score. The features that make use of a weighted model that determines which entity types are most important and which have a larger weight and determine how much reliably the extraction of the correct features is performed based on the confidence score, May be employed.

5 is a flow diagram illustrating a method 500 of generating a search suggestion word using fuzzy-score matching and entity co-occurrence in a knowledge base. The method 500 may be implemented in a search system 400 similar to that shown in FIG.

In one embodiment, the method 500 is initiated by the user starting typing a search query at step 502 into the search engine interface 402 shown in FIG. As the search query is typed in step 502, the search system 400 executes an on-the-fly process. According to various embodiments, the search query input at step 502 may be complete, or partially spelled or misspelled. Subsequently, in the search system 400, the partial entity extraction step 504 from the search query input of step 502 may be executed. The partial entity extraction step 504 performs a quick search for the entity concurrency knowledge base 414 to identify if the search query entered in step 502 is an entity and if so, what the type of entity is. According to various embodiments, the search query entry of step 402 may be, among other things, referring to a person, organization, location and date of a location. If the entity type of the search query input is identified, the fuzzy-score matching module 412 may select a corresponding fuzzy matching algorithm at step 506. [ For example, if the search query has been identified as an entity that refers to a person, the fuzzy-score matching module 412 may determine the fuzzy-score matching module 412 based on, for example, the first, middle, And then selects a string matching algorithm for the person. In another embodiment, if the search query is identified as an entity that refers to an organization, the fuzzy-score matching module 412 may use a string matching algorithm for an organization that may include identifying terms such as schools, colleges, corporations, corporations, Can be selected. The fuzzy-score matching algorithm 412 may then select a string matching algorithm corresponding to the type of identified entity in the search query input to outperform its search. If the string matching algorithm is adjusted to the type of entity being identified, a fuzzy-score matching step 508 may be performed.

In the fuzzy-score matching step 508, extracted entities or entities and non-entities are searched and compared to the entity concurrency knowledge base 414. The extracted entities or entities may include, among others, an incomplete name of a person, such as, for example, the first character of a first and last name, an abbreviation of an organization such as "UN" a short form, and an alias. The entity concurrency knowledge base 414 may include among other things a plurality of records indexed as structured data such as entity to entity, entity to topic, and entity to fact May be registered in advance. The latter allows the fuzzy-score matching at step 508 to be done in a very fast manner. The fuzzy-score matching at step 508 may use conventional string metrics such as, but not limited to, Levenshtein distance, strcmp95, ITF scoring. The Levenshtein distance between two words refers to the minimum number of single-character edits necessary to change one word to another.

Finally, when the fuzzy-score matching step 508 finishes comparing and searching all the records in the entity concurrent knowledge base 414 with the search query, at step 510, the given pattern string (i.e., the search query of step 502) Input) may be selected as the first candidate for the search result word. Other records that matched less closely with a given pattern string are placed in the downward order below the first candidate. The search suggestion at step 510 may be provided to the user as a drop down list of possible matches that the user may ignore or ignore.

FIG. 6 is an exemplary user interface 600 according to a method for generating a search suggestion word using fuzzy-score matching and entity co-occurrence in a knowledge base, as shown in FIGS. 4 and 5 above. In this example, the user enters a partial query 604 into the search box 606 via a search engine interface 602 similar to that shown in FIG. By way of example, and not by way of limitation, the partial query 604 may be an incomplete name of a person such as "Michael J ", as shown in FIG. Because the user may not yet have selected the navigation button 608 to perform the actual search and obtain the result or else the partial query 604 may not have been provided to the search system 400 ) Can be considered.

Following the method 500 (FIG. 5), as the user types "Michael J ", the entity extraction module 410 generates an instant quick search of the first word Michael for the entity concurrent knowledge base 414 quick search on the fly) to identify the type of entity. In this example, the entity may refer to a person's name. Consequently, the fuzzy-score matching module 412 may select a string matching algorithm that is appended to the person's name. A person's name may be found in a database written in another form, for example, using only the first character of the initial (abbreviation) or first name, or the first character of the last name, middle name and last name, . The fuzzy-score matching module 412 may use a conventional string metric, such as a Levenshtein distance, to determine and assign a score for an entity, topic or fact in the entity concurrency knowledge base 414 that can match the entity "Michael & Can be used. In this example, Michael matches a significant amount of records with that name. However, as the user types the succeeding character "J ", the fuzzy-score matching module 412 performs another comparison of all concurrent occurrences with entity concurrency knowledge base 414 and Michael based on the Levenshtein distance can do. The entity concurrency knowledge base 414 selects all possible matches with the highest score for "Michael J ". For example, the fuzzy-score matching module 412 may in some cases return search suggestions 610 such as "Michael Jackson", "Michael Jordan", "Michael J. Fox", or "Michael Dell" have. The user can select one of the suggested persons from the drop-down list to complete the search query. Expanding on the above example, an inquiry such as "Michael the basketball player" can be found in "the Michael" in the transformation of human entity names, and in "the basketball player" in concurrent features such as key phrases, facts and topics Based on the results returned by the search of the co-occurrence knowledge base for the entity, it is possible to derive the suggested term "Michael Jordan". Another example could be "Alexander the actor", leading to the suggestion "Alexander Polinsky". Those skilled in the art will appreciate that an existing search platform can not generate a presentation word in the manner described above.

7 is a block diagram of a search system 700 in accordance with the present disclosure. The search system 700 may include one or more user interfaces 702 for a search engine 704 that communicates with a server device 706 via a network 708. The search engine 704 may include a search engine 704, In this embodiment, the search system 700 may be implemented in a client / server type architecture. However, the search system 700 may include other computer architectures (e.g., a stand alone computer, a mainframe system with terminals, an ASP model, a peer-to-peer model, etc.) , A local area network (LAN), a wide area network (WAN), the Internet, a wireless network, a mobile phone network, and the like.

The search engine 704 includes, but is not limited to, an interface through a web-based tool that allows a user to locate information on the World Wide Web. The search engine 704 may include a tool that allows the user to locate information within the internal database system. The server device 706, which may be implemented in a distributed architecture that is implemented in a single server device 706 or spans multiple server computers, includes an entity extraction module 710, a fuzzy-score matching module 712, And a knowledge base database 714.

Entity extraction module 710 may comprise hardware and / or software configured to extract on-the-fly independent entities from a given query set, such as query strings, partial queries, structured data, etc., Module. For example, the entities may include a person, an organization, a geographic location, a date and / or a time. During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of the feature attribute, the relative weight and relevance of each of the features can be determined. Additionally, the relevance of association between features may be determined using a weighted scoring model.

The fuzzy-score matching module 712 may include a number of algorithms that may be adjusted or selected according to the entity type extracted from a given search query. The function of the algorithm is to determine whether the given search query (input) and presented search strings are similar to each other or whether the given search string matches appropriately with a given pattern string. Fuzzy matching is known as fuzzy string matching, inexact matching, and approximate matching. Entity extraction module 710 and fuzzy score matching module 712 work in conjunction with entity concurrency knowledge base 714 to generate search suggestions for users.

According to various embodiments, the entity concurrency knowledge base 714 may be constructed as an in-memory database and may include components such as one or more search controllers, a plurality of search nodes, a collection of compressed data, But are not limited to, One search controller may optionally be associated with one or more search nodes. Each search node can independently perform a fuzzy key search through a collection of compressed data and return a set of scored results to its associated search controller.

The entity co-occurrence knowledge base 714 may include related entities based on features and ranked by a trust score. The features that make use of a weighted model that determines which entity types are most important and which have a larger weight and determine how much reliably the extraction of the correct features is performed based on the confidence score, May be employed.

FIG. 8 is a flow diagram illustrating an embodiment of a method 800 of generating a search suggestion of related entities based on concurrency and / or fuzzy score matching. The method 800 may be implemented in a search system 700 similar to that shown in FIG.

In one embodiment, the method 800 is initiated by the user initiating a search query at step 802 in the search engine 702 shown in FIG. As the search query is typed, the search system 700 executes an on-the-fly process. In various embodiments, the search query may be complete and / or partially spelled correctly or spelled incorrectly. Next, the partial entity extraction step 804 of the search query can be executed. The partial entity extraction step 804 performs a quick search for the entity concurrency knowledge base 714 to identify whether the search query includes an entity and, if so, the type of entity. According to various embodiments, the search query entity may refer to a person, an organization, a location and a date among other things. If there is an entity, the fuzzy-score matching module 712 may select the corresponding fuzzy matching algorithm at step 806. [ For example, if the search query has been identified as an entity that refers to a person, the fuzzy-score matching module 712 may determine that the fuzzy- And adjusts or selects a string matching algorithm for the person that can extract the strings. In another embodiment, if the search query is identified as an entity that refers to an organization, the fuzzy-score matching module 712 may use a string matching algorithm for organizations that may include identifying terms such as schools, colleges, corporations, corporations, Can be adjusted or selected. Thus, the fuzzy-score matching algorithm 712 may adjust or select a string matching algorithm for the type of entity to facilitate its search. If the string matching algorithm is adjusted or selected to correspond to the type of entity, then fuzzy-score matching may be performed at step 808. [

In the fuzzy-score matching step 808, the extracted entities or entities and any non-entities are searched and compared to the entity concurrency knowledge base 714. The extracted entities or entities may include, among others, an incomplete name of a person, such as, for example, the first character of a first and last name, an abbreviation of an organization such as "UN" a short form, and an alias. The entity concurrency knowledge base 714 may be any of a number of indexed to structured data, such as entity to entity, entity to topic, and entity to facts index among others. May be registered in advance. This allows the fuzzy-score matching at step 808 to be done quickly. Fuzzy-score matching can use conventional string metrics such as, but not limited to, Levenshtein distance, strcmp95, ITF scoring. The Levenshtein distance between two words refers to the minimum number of single-character edits necessary to change one word to another.

If the fuzzy-score matching step in step 808 finishes comparing and searching all the records in the entity concurrency knowledge base 414 with the search query, then in step 801, the best match with the given pattern string of the search query input Or the closest matched record may be selected as the first candidate for the search presentation word. The other records matched less closely to the given pattern string of the search query input are placed in the downward order below the first candidate. The search suggestion at step 810 may be provided to the user as a drop down list of possible matches that the user can select to complete the query.

In another embodiment, after the user selects a match of his / her interest, the search system 700, in step 812, takes the selection as a new search query. Subsequently, the entity extraction step 814 from the new search query may be executed. During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of the feature attribute, the relative weight and relevance of each of the features can be determined. Additionally, the relevance of association between features may be determined using a weighted scoring model. The entity extraction module 710 may perform a search for the entity concurrency knowledge base 714 to discover related entities at step 816 based on concurrency with the highest scores. Finally, before executing the actual search of the data in the electronic document corpus, a drop-down list of search suggestions at step 818, including relevant entities, may be provided to the user.

FIG. 9 is an exemplary embodiment of a user interface 900 associated with a method 800 for generating search suggestions of related entities based on coincidence and / or fuzzy score matching. In this example, the user enters a partial query 904 into the search box 906 via a search engine interface 902 similar to that shown in FIG. By way of example, and not by way of limitation, the partial query 904 may be an incomplete name of a person such as "Michael J ", as shown in FIG. Because the user may not yet have selected the navigation button 908 to perform the actual search and obtain the result or else it may not have provided the partial query 604 to the search system 100, ) Can be considered.

Following the method 800, as the user types "Michael J ", the entity extraction module 710 generates a quick search on the first word Michael for the entity concurrency knowledge base 714 fly) to identify the type of entity. In this example, the entity may refer to a person's name. Subsequently, the fuzzy-score matching module 712 may select a string matching algorithm that is appended to the person's name. A person's name may be found in a database written in another form, for example, using only the first character of the initial (abbreviation) or first name, or the first character of the last name, middle name and last name, . The fuzzy-score matching module 712 may use a conventional string metric, such as a Levenshtein distance, to determine and assign a score for an entity, topic or fact in the entity concurrency knowledge base 714 that may match the entity "Michael & Can be used. In this example, Michael matches a significant amount of records with that name. However, as the user types the following character "J ", the fuzzy-score matching module 712 performs another comparison of all concurrent occurrences with the entity concurrency knowledge base 714 and Michael based on the Levenshtein distance can do. The entity concurrency knowledge base 714 selects all possible matches with the highest score for "Michael J ". For example, the fuzzy-score matching module 712 may in some cases generate a search suggestion (" Michael J ") to complete a "Michael J" such as "Michael Jackson "," Michael Jordan & 910) to the user. The user can select one of the presented persons from the drop-down list, or ignore the suggested word and continue typing. Expanding on the above example, an inquiry such as "Michael the basketball player" can be found in "the Michael" in the transformation of human entity names, and in "the basketball player" in concurrent features such as key phrases, facts and topics Based on the results returned by the search of the co-occurrence knowledge base for the entity, it is possible to derive the suggested term "Michael Jordan". Another example could be "Alexander the actor", leading to the suggestion "Alexander Polinsky". Those skilled in the art will appreciate that existing search platforms can not provide a presentation word that is generated in the manner described above.

In this embodiment, the user can select "Michael Jordan" from the drop-down list to complete the partial query 904 shown in FIG. The selection may be processed by the search system 700 as a new search query 912. Subsequently, entity extraction from the new search query may be performed. During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of the feature attribute, the relative weight and relevance of each of the features can be determined. Additionally, the relevance of association between features may be determined using a weighted scoring model. Entity extraction module 710 may perform a search for "Michael Jordan" for entity concurrency knowledge base 714 to discover related entities based on concurrency with the highest scores. Finally, a drop-down list of search suggestion words 914 may be provided to the user, including relevant entities, prior to performing an actual search by clicking the search button 908. [ The systems and methods described above in FIGS. 7-9 are fast and convenient to the user, since the user may find useful relevance.

10 is a block diagram of a search system 1000 in accordance with the present disclosure. The search system 1000 may include a search engine 1002, which may include one or more user interfaces that allow data input from a user, such as user queries.

The search system 1000 may include one or more databases. Such databases may include an entity database 1004 and a trend database 1006. The databases may be stored on a local server or a web based server. Thus, while the search system 1000 can be implemented in a client / server type architecture, the search system 1000 can be, for example, a stand alone computer, a mainframe system with terminals, an ASP model, a peer- peer-to-peer model, and the like, and multiple networks, such as, for example, a local area network (LAN), a wide area network (WAN), the Internet, a wireless network, .

Search engine 1002 includes, but is not limited to, an interface through a web-based tool that allows a user to locate information on the World Wide Web. The search engine 1002 may include a tool that allows the user to locate information within the internal database system.

The entity database 1004 may be implemented in a distributed architecture that spans a single server or multiple servers. The entity database 1004 allows a set of entity references, such as query strings, structured data, and so on. Such an entity lookup set may be pre-extracted from a plurality of corpuses available in the Internet and / or the local network. Entity queries are indexed and scored. For example, the entities include a person, an organization, a geographic location, a date and / or a time. During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of the feature attribute, the relative weight and relevance of each of the features can be determined. Additionally, the relevance of association between features may be determined using a weighted scoring model.

The trend database 1006 may be implemented in a distributed architecture that spans a single server or multiple servers. The trend database 1006 allows for a set of entity hits such as query strings, structured data, and so on. Such an entity query set may be pre-extracted from a history query run by a user and / or multiple users on the Internet and / or the local network. Entity queries are indexed and scored. For example, the entities include a person, an organization, a geographic location, a date and / or a time. During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of the feature attribute, the relative weight and relevance of each of the features can be determined. Additionally, the relevance of association between features may be determined using a weighted scoring model.

Entity database 1004 and trend database 1006 may include an entity concurrent knowledge base that may be constructed as an in-memory database (not shown) and may include one or more search controllers, a plurality of search nodes, And other components (not shown), such as a collection of messages and a conflict resolution module. One search controller may be selectively associated with one or more search nodes. Each search node can independently perform a fuzzy key search through a collection of compressed data and return a set of scored results to its associated search controller.

A concurrent knowledge base may include related entities based on features and ranked by a trust score. The features that make use of a weighted model that determines which entity types are most important and which have a larger weight and determine how much reliably the extraction of the correct features is performed based on the confidence score, various methods of linking may be employed.

The search system 1000 can compare a user query in the search engine 1002 with an entity database 1004 and a trend database 1006. Both databases, The autocomplete mode for search engine 1002 may be enabled. The search system 1000 deploys a list of search suggestion words 1008 to the user, which is generated and indexed based on the fuzzy score assigned to each entity word in the database. The score of each entity presenter may be manually assigned by the search system 1000, manually by the system supervisor. The entity presenter is sorted from the most relevant to the less relevant based on the score achieved by each entity. In addition, the scores in the trend database 1006 may be assigned using trends and query frequencies from one or more users in the local network and / or the Internet.

The entity presenter of each database is compared to each other, indexed, and sorted by the rank obtained in the score, so that the search presenter list 1008 is associated with both databases, i. E., Entity database 1004, And is displayed to the user who combines the entity presenter in the database 1006. If the user selects a presentation word from the list or selects a result other than the presenter list, the search system 1000 may store such information in the trend database. Thus, a self-learning system becomes possible, increasing the reliability and accuracy of the search system 1000. Briefly, the trend co-occurring knowledge base is continuously updated with the features extracted from the user's query and the selected presentation word, thereby improving on-the-fly relevance and accuracy by providing on-the-fly learning. In addition, a trend co-occurrence knowledge base can be populated by an automatic method such as another user using the system and a trend search module.

11 is a block diagram of a search system 1100 in accordance with the present disclosure. The search system 1100 includes a search engine 1102 and such search engine 1102 may include one or more user interfaces that allow data input from a user, such as user queries.

The search system 1100 may include one or more databases. Such databases may include an entity database 1104 and a trend database 1106. The databases may be stored on a local server or a web based server. Thus, while the search system 1100 can be implemented in a client / server type architecture, the search system 1100 can be, for example, a stand alone computer, a mainframe system with terminals, an ASP model, a peer- peer-to-peer model, and the like, and multiple networks, such as, for example, a local area network (LAN), a wide area network (WAN), the Internet, a wireless network, .

In one embodiment, search system 1100 may begin when a user enters one or more entities (in search queries) through a user interface in search engine 1102. For example, the search query may be a combination of keywords in a string data format, structured data, and the like. These keywords may be entities representing a person, organization, geographic location, date and / or time. In the present embodiment, "Indiana Na" is used as a search query.

"Indiana Na" can be processed for entity extraction. The entity extraction module processes search queries such as "Indiana Na " as entities and compares them all with the entity concurrent knowledge base in the entity database 1104 and trend database 1106 to extract as many entities as possible, Resolve sexuality. Additionally, query text portions that are not detected as entities (e.g., person, organization, location) may include conceptual features that may be employed to search for an entity concurrent knowledge base (e.g., entities and trench databases) (E.g., topic, fact, key phrase). During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. In consideration of feature attributes, a relative weight or relevance of each feature can be determined. Additionally, the relevance of associations between features can be determined using a weighted scoring model.

In the present embodiment, the entity database 1104 shows the list of search prescriptions as a list 1108 of entity presets that can be indexed and ranked. The trend database 1106 shows the list of search suggestions as a list of trend-based presenters 1110 that can be indexed and ranked. Subsequently, the search system 1100 may build the search suggestion list 1112 based on those provided by the entity database 1104 and the trend database 1106. [ The search presenter list 1112 can be indexed and ranked based on the individual scores of each entity's presenter in each database so that the most relevant first appears first and the less relevant results follow below.

In search system 1100, an exemplary use for obtaining a search suggestion word is disclosed. The search suggestion word list 1112 shows the presentation words based on the "Indiana Na" user inquiry. As a result, "Indiana Name" appears first based on an individual score of 0.9 for that entity, then "Indiana Nascar" is seen as the result of an individual score of 0.8, and finally "Indiana Nashville" 0.0 > 0.7 < / RTI > The individual scores may be compared using a list of entity presenter 1108 and a list of trend-based presenters 1110 without considering the consideration of repeated entities.

12 is a block diagram of a search system 1200 in accordance with the present disclosure. The search system 1200 includes a search engine 1202 and such search engine 1202 may include one or more user interfaces that allow data input from a user, such as user queries.

The search system 1200 may include one or more databases. Such databases may include an entity database 1204 and a trend database 1206. The databases may be stored on a local server or a web based server. Thus, while the search system 1200 may be implemented in a client / server type architecture, the search system 1200 may include, for example, a stand alone computer, a mainframe system with terminals, an ASP model, a peer- peer-to-peer model, and the like, and multiple networks, such as, for example, a local area network (LAN), a wide area network (WAN), the Internet, a wireless network, .

In one embodiment, search system 1200 may begin when a user enters one or more entities (search queries) through a user interface in search engine 1202. For example, the search query may be a combination of keywords, such as strings, structured data, and the like. These keywords may be entities representing a person, organization, geographic location, date and / or time. In the present embodiment, "Indiana Na" is used as a search query.

"Indiana Na" can be processed for entity extraction. The entity extraction module processes search queries such as "Indiana Na " as entities and compares them all with the entity concurrent knowledge base in the entity database 1204 and trend database 1206 to extract as many entities as possible, Resolve sexuality. Additionally, query text portions that are not detected as entities (e.g., person, organization, location) may include conceptual features that may be employed to search for an entity concurrent knowledge base (e.g., an entity database and a trent database) (E.g., topic, fact, key phrase). During extraction, one or more feature recognition and extraction algorithms may be employed. In addition, a score may be assigned to each extracted feature indicating that the certainty level of the feature is correctly extracted with the correct attribute. Based on each feature attribute, the relative weight or relevance of each feature can be determined. Additionally, the relevance of associations between features can be determined using a weighted scoring model.

In the present embodiment, the entity database 1204 shows the list of search prescriptions as a list of indexed and ranked entity presets 1208 that are already indexed. Similarly, the trend database 1206 shows a list of search suggestions as an indexed and ranked trend-based presenters list 1210 already indexed. Subsequently, the search system 1200 may build the search suggestion list 1212 based on those provided by the entity database 1204 and the trend database 1206. [ The search suggestion list 1212 can be indexed and ranked based on the overall score of each entity's presenter in both databases, so that the most relevant first appears first and the less relevant result goes below.

In search system 1200, an exemplary use for obtaining a search suggestion word is disclosed. The search suggestion word list 1212 shows the presentation words based on the "Indiana Na" user inquiry. As a result, "Indiana Nascar" is first shown based on an overall score of 1.4 resulting from the sum of score 0.8 in the entity presenter list 1208 and score 0.6 in the trend-based presenter list 1210. Similarly, the "Indiana Name" is shown as a result of the overall score of 0.9, and finally the Indiana Nashville "is shown based on the overall score of 0.7.

13 is a system architecture 1300 for geotagging content in SharePoint 2013®. The search index 1324 is one of a number of key components to enable searching in the SharePoint 1302. Another key portion that allows navigation in SharePoint 2013® 1302 can be captured content to index the content. SharePoint 1302 includes a crawler 1304 component for content capture.

The crawler 1304 may crawl through other content sources 1306 that add a list of metadata properties to each content. For example, a content source may include SharePoint content, network file-sharing, or user or Internet content without limitation. The crawler 1304 may be configured to perform secure connection functions to the content source 1306, which associates the documents from the source with their metadata as crawled properties. The crawler 1304 is configured for full or incremental crawls for the content. For example, the crawled property may include, among other things, author, title, and creation date, without limitation, for example.

SharePoint 2013® includes a content processing 1308 component. The content processing 1308 component takes content from the crawler 1304 and is ready to index it. Content processing 1308 may include, among others, verbal processing (language detection), parsing, entity extraction management, content based file format detection, content processing error reporting, natural language processing, and managed properties ). ≪ / RTI >

Content processing 1308 may be extended by a content enrichment web service (CEWS) 1310. The CEWS 1310 may be augmented with the content processing 1308 by allowing the web service callout 1312 to invoke an external web service to perform additional operations and augment the crawled data characteristics I will. The web service callout 1312 may be a standard simple object access protocol (SOAP) request or any other web service invocation method used to exchange the structured information of the crawled data with the entity enrichment service 1314 . Web service callout 1312 may include trigger conditions configured in a content augmented configuration object that control when invoking an external web service for augmentative processing. In addition, the entity enhancement service 1314 can determine the document type of the crawled data to determine the content that comes in the form of an image (scanned documents, pictures, etc.). Whenever an image-type of content is found, the entity enrichment service 1314 directs the location of the crawled document to an OCR processing engine 1316, such as (without limitation) an optical character recognition component or other image processing component, Lt; / RTI > OCR processing engine 1316 can retrieve and process image files and asynchronously convert them to text files. Subsequently, the OCR processed files 1318 are fed back to the crawler 1304, crawled as text files, and sent back to the content processing 1308 to proceed to the rest of the workflow.

The system architecture 1300 includes an external geotagger web service 1320 and a named entity tagger service 1322. The geotagger web service 1320 and the named entity tager service 1322 may function as a web service application provider and be configured to respond to a web service callout 1312. The GeoTagger web service 1320 can utilize natural language processing entity extraction techniques, machine learning models, and other techniques to identify geographic entities from crawled content and resolve their ambiguities. For example, geotagger web service 1320 can resolve the ambiguity of geographic entities by analyzing the statistical co-occurrence of entities found in a gazetteer. The GeoTagger web service 1320 may include a database of statically concurrent entities that can be linked to the content found by the crawler 1304. Following the same technique, the named entity tiger service 1322 can be used to extract additional entities or text features such as an organization, person, or topic.

Geotagger web service 1320 can analyze an array of management properties sent as input properties by CEWS 1310 and identify any geographic entities referenced in the text. By way of a non-limiting example, input properties may include, among others, FileType, IsDocument, OriginalPath, and body. The geotagger web service 1320 can geotag the text by creating and modifying management properties with reference to each geographical entity found. Geotagger Web service 1320 may send modified or new management properties to entity enhancement service 1314 where a transformation is made to map the modified management properties and return them as output properties to CEWS 1310. [ The same process can be used for the extraction of other entities or text features, such as an organization, person or topic, and for interaction with the named entity tiger service 1322 for entity tagging.

After the enhanced management properties are returned by the entity enhancement service 1314, the properties are merged with the crawled file management properties and sent to the search index 1324. [

If geographic and other entity tags have been indexed and associated with the content, search queries are performed using geographic and named entity properties. The search UI 1326 in SharePoint 2013® may include a particular display that supports the user in performing geographic based searches and can support enhanced display of faceted search results. The navigation UI 1326 can be a custom web portion or can be implemented by modifying the standard layout of SharePoint 2013® navigation with standard tools such as HTML, HTML 5, JavaScript and CSS.

14 is a flowchart 1400 illustrating process steps for tagging content for SharePoint 2013® search. The process begins when the crawler component in SharePoint 2013® performs a crawl on the content (step 1402). In one embodiment, the claw may be a full crown, and in another embodiment, the crown may be an increasing crown. The crawler component feeds the crawled properties and metadata to the content processing (step 1404). A determination may be made to prove that the crawled content includes a geographically or named entity. Exemplarily and without limitation, a trigger condition may be used. The trigger condition may include program logic or a set of rules that determine whether the content can benefit from geotagging or entity tagging. If the trigger condition is evaluated to be false, the crawled content is associated with the management properties (step 1406) and passed to the search index component (step 1408). If the trigger condition evaluates to true, the CEWS sends a web service callout (step 1410) to the entity enrichment service. To determine if the content is in an image format (scanned documents, pictures, etc.), the entity augmentation service may analyze the transferred content. The content found in the image format is processed asynchronously by the OCR and sent back as a text file to be re-crawled by the crawling component (step 1412). If the content is not in an image format, the content may be processed by the geotagging web service or the name entity tiger service (step 1414). Web services extract geographic or named entities referenced in the content, resolve their ambiguities, and reinforce them with entity metadata. The identified entities and their metadata are sent back to the content processing component as management properties and associated with the content (step 1416). Associated metadata may be sent to the search index component (step 1406).

While various aspects and embodiments have been disclosed, other aspects and embodiments are contemplated. The various aspects and embodiments disclosed are for the purpose of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

It should be understood that the description of the methods and process flow diagrams above are provided as illustrative examples only and are not intended to imply or imply that the steps of the various embodiments should be performed in the order presented. As will be appreciated by those of ordinary skill in the art, the steps in the above-described embodiments may be performed in any order. The words "then "," next ", and the like are not intended to limit the order of the steps, and such words are used merely to guide the reader throughout the description of the method. Although the process flow diagram can describe the operation as a sequential process, many operations can be performed in parallel or concurrently. Also, the order of operations can be rearranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, and the like. When a process corresponds to a function, the termination of the process may correspond to the calling function or the return of the function to the main function.

The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this substitution of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

Embodiments embodied in computer software may be implemented in software, firmware, middleware, microcode, hardware description language, or any combination thereof. A code segment or machine executable instruction may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or hardware circuit by communicating and / or receiving information, data, arguments, parameters or memory contents. Information, arguments, parameters, data, etc. may be communicated, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing,

The actual software code or dedicated control hardware used to implement such systems and methods does not limit the present invention. Thus, the operation and operation of systems and methods have been described without reference to specific software code that is understood to be software and control hardware that may be designed to implement systems and methods based on the description herein

When implemented in software, the functions may be stored in one or more instructions in non-volatile computer readable or processor readable storage media. The steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module that may reside on a computer-readable or processor-readable storage medium. Non-volatile computer readable or processor readable media include both computer storage media and types of storage media that facilitate transferring computer programs from one place to another. Non-volatile processor readable storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such non-volatile processor readable media can be RAM, ROM, EEPROM, CD-ROM, CD-ROM, Or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other type of storage medium. As used herein, discs and discs include compact discs (CDs), laser discs, optical discs, multifunctional discs (DVD), floppy discs, and Blu-ray discs, While discs reproduce data in a magnetic way, discs reproduce data in an optical manner using a laser. Combinations of the foregoing should also be included within the scope of computer readable media. In addition, the operation of the method or algorithm may reside in any combination or combination of one or more of the codes and / or instructions on a non-transitory processor readable medium and / or computer readable medium that may be incorporated into the computer program product .

It will be appreciated that the various components of the technology may be deployed in a remote portion of the distributed network and / or the Internet, or in a dedicated secure, unsecured, and / or encrypted system. It is therefore to be appreciated that components of the system may be combined into one or more devices or may coexist on a particular node of a distributed network, such as a communications network. As will be appreciated from the description, and for reasons of computational efficiency, the components of the system can be arranged anywhere in the distributed network without affecting the operation of the system. Moreover, the components may be embedded in a dedicated machine.

In addition, the various links connecting the elements may be wired or wireless links, or any combination thereof, that can feed and / or transmit data to and from the connected elements, or any other known or later developed Lt; / RTI > element (s). The term module as used herein may refer to any known or later developed hardware, software, firmware, or a combination thereof capable of performing the functionality associated with the element. As used herein, the terms determining, arithmetic and computation, and variations thereof are used interchangeably and include any form of methodology, process, mathematical operation or technique.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those of ordinary skill in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. The present invention, therefore, is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein, as well as the following claims.

The foregoing embodiments are intended to be illustrative. Those of ordinary skill in the art will recognize that many alternative components and embodiments may be substituted for the specific examples described herein and still fall within the scope of the invention.

Claims (56)

As a computer implemented method,
The entity extraction computer receiving a search query from the client computer having one or more entities;
The entity extraction computer comparing each entity with one or more co-occurrences of each entity in the co-occurrence database;
Wherein the entity extraction computer extracts a subset of one or more entities from a search query, wherein the extraction is based on a coherence level of concurrency of the entity and one or more related entities in an electronic data corpus according to the co- In response to determining that each entity exceeds the trust score of the co-occurrence database;
The entity extraction computer assigns an index identifier (index ID) to each of the entities within the plurality of extracted entities;
The entity extraction computer storing an index ID for each of a plurality of extracted entities in an electronic data corpus indexed by an index ID corresponding to each of one or more related entities;
A search server computer is configured to search for an entity indexed electronic data corpus to locate index identifiers of data records in which at least two of the plurality of extracted entities are co- ≪ / RTI >
Wherein the search server computer comprises constructing a search result list having a data record corresponding to the identified index IDs
Computer implemented method.
The method according to claim 1,
The search server computer sorting the search result list by relevance based on the trust score,
The search server computer further comprising transmitting the sorted search result list to the user device
Computer implemented method.
The method according to claim 1,
The plurality of extracted entities are ranked based on the trust score
Computer implemented method.
The method according to claim 1,
The entity extraction computer associates the extracted entity with one or more co-occurring entities in the entity index electronic data corpus
Computer implemented method.
5. The method of claim 4,
The associated entities are ranked by the trust score
Computer implemented method.
The method according to claim 1,
Each of the plurality of entities is selected from the group comprising a person, an organization, a geographic location, a date and a time
Computer implemented method.
As a system,
One or more server computers with one or more processor executable computer readable instructions for a plurality of computer modules,
The plurality of computer modules,
An entity extraction module configured to receive user input of search query parameters; And
A search server module,
Wherein the entity extraction module comprises:
Extracting a plurality of entities from the search query parameters wherein the extracting comprises an entity concurrency database comprising an extracted entity and a confidence score representing a concurrency level of coincidence of one or more related entities in the electronic data corpus, With the respective entities in the extracted entities of the entity;
Assigning an index identifier (index ID) to each entity in the plurality of extracted entities;
To store an index ID for each of a plurality of extracted entities in an electronic data corpus indexed by an index ID corresponding to each of one or more associated entities,
The search server module,
For locating a plurality of extracted entities and for identifying at least two of the plurality of extracted entities the index IDs of concurrently occurring data records;
And to construct a search result list having a data record corresponding to the identified index ID
system.
8. The method of claim 7,
Wherein the search server module is further configured to sort the search result list by relevance based on the trust score and to send the sorted search result list to the user device
system.
8. The method of claim 7,
The plurality of extracted entities are ranked based on the trust score
system.
8. The method of claim 7,
The entity extraction module is configured to associate the extracted entity with one or more concurrent entities in the entity index electronic data corpus
system.
11. The method of claim 10,
The associated entities are ranked by the trust score
system.
8. The method of claim 7,
Each of the plurality of entities is selected from the group comprising a person, an organization, a geographic location, a date and a time
system.
17. A non-transitory computer readable medium having computer executable instructions stored thereon,
The computer-
An entity extraction computer to receive user input of search query parameters;
Wherein the entity extraction computer causes a plurality of entities to be extracted from the search query parameters, the extraction including a confidence score indicating a concurrency level of concurrency of the extracted entity and one or more related entities in the electronic data corpus To be performed by comparing the entity concurrency database with each entity in the plurality of extracted entities;
The entity extraction computer to assign an index identifier (index ID) to each entity in the plurality of extracted entities;
The entity extraction computer storing an index ID for each of a plurality of extracted entities in an electronic data corpus indexed by an index ID corresponding to each of one or more related entities;
The search server computer causes the entity index electronic data corpus to be searched to locate a plurality of extracted entities and to identify an index ID of a concurrently occurring data record of at least two of the plurality of extracted entities;
Wherein the search server computer is configured to construct a search result list having a data record corresponding to the identified index ID,
Non-transient computer readable medium.
14. The method of claim 13,
Wherein the command comprises:
Wherein the search server computer causes the search result list to be sorted by relevance based on the trust score,
Further comprising causing the search server computer to transmit the sorted search result list to the user device
Non-transient computer readable medium.
14. The method of claim 13,
The plurality of extracted entities are ranked based on the trust score
Non-transient computer readable medium.
14. The method of claim 13,
Wherein the command comprises:
Wherein the entity extraction computer is further configured to associate the extracted entity with one or more co-occurring entities in an entity index electronic data corpus
Non-transient computer readable medium.
17. The method of claim 16,
The associated entities are ranked by the trust score
Non-transient computer readable medium.
14. The method of claim 13,
Each of the plurality of entities is selected from the group comprising a person, an organization, a geographic location, a date and a time
Non-transient computer readable medium.
An entity extraction computer receives a user input of search query parameters from a user interface;
Wherein the entity extraction computer compares search query parameters with an entity concurrency database having instances of concurrent occurrence of one or more entities in an electronic data corpus and determines at least one of at least two entities corresponding to one or more entities in the search query parameters Extract one or more entities from the search query parameters by identifying one entity type;
Selecting a fuzzy matching algorithm for searching an entity coincidence database to identify one or more records associated with search query parameters, the fuzzy matching algorithm corresponding to at least one identified entity type, ;
Wherein the fuzzy-score matching computer searches the entity concurrency database using the selected fuzzy matching algorithm and forms one or more suggested search query parameters from the one or more records based on the search;
Wherein the fuzzy-score matching computer comprises providing one or more suggested search query parameters via a user interface
Way.
20. The method of claim 19,
Wherein the fuzzy-score matching computer further comprises searching the entity co-occurrence database using the selected fuzzy matching algorithm before the user input is terminated
Way.
20. The method of claim 19,
One or more records associated with the search query parameters may contain conceptual features
Way.
20. The method of claim 19,
One or more suggested search query parameters include a number of suggested search query parameters,
The method comprises:
Wherein the fuzzy-score matching computer further comprises sorting the plurality of presented search query parameters in a downward order based on a proximity of a match for the search query parameters in the user input
Way.
23. The method of claim 22,
The fuzzy-score matching computer may be configured to provide, via the user interface, a plurality of suggested search query parameters sorted into a drop-down list
Way.
20. The method of claim 19,
Entity Concurrency Database is Indexed
Way.
The method according to claim 1,
Entity Concurrency A database contains an entity-to-entity index
Way.
20. The method of claim 19,
An entity-concurrency database is one that contains an entity-to-topic index
Way.
20. The method of claim 19,
An entity-concurrency database contains an entity-to-facts index
Way.
Comprising one or more server computers with one or more processor executable computer readable instructions for a plurality of computer modules,
The plurality of computer modules,
An entity extraction module;
A fuzzy-score matching module,
The entity extraction module,
Configured to receive user input of search query parameters from a user interface;
Compare search query parameters with an entity concurrency database having instances of concurrent occurrence of one or more entities in an electronic data corpus and identify at least one entity type corresponding to one or more entities in search query parameters To extract one or more entities from the search query parameters,
The fuzzy-
Selecting a fuzzy matching algorithm to search for an entity coincidence database to identify one or more records associated with search query parameters, the fuzzy matching algorithm corresponding to at least one identified entity type;
Searching the entity concurrency database using the selected fuzzy matching algorithm, and forming one or more suggested search query parameters from the one or more records based on the search;
Further configured to provide one or more suggested search query parameters via the user interface
system.
29. The method of claim 28,
The fuzzy-score matching module is further configured to search the entity concurrency database using the selected fuzzy matching algorithm before the user input is terminated
system.
29. The method of claim 28,
One or more records associated with the search query parameters may contain conceptual features
system.
29. The method of claim 28,
One or more suggested search query parameters include a number of suggested search query parameters,
The fuzzy-score matching computer is further configured to sort the plurality of presented search query parameters in a downward order based on a proximity of a match for the search query parameters in the user input
system.
33. The method of claim 32,
The fuzzy-score matching computer is configured to provide, via a user interface, a plurality of suggested search query parameters sorted into a drop down list
system.
29. The method of claim 28,
Entity Concurrency Database is Indexed
system.
29. The method of claim 28,
Entity Concurrency A database contains an entity-to-entity index
system.
29. The method of claim 28,
An entity-concurrency database is one that contains an entity-to-topic index
system.
29. The method of claim 28,
An entity-concurrency database contains an entity-to-facts index
system.
An entity extraction computer receives a user input of partial search query parameters - partial search query parameters from the user interface having at least one incomplete search query parameter;
Entity extraction computer compares the partial search query parameters with an entity concurrency database having instances of concurrent occurrence of one or more first entities in the electronic data corpus and determines whether the partial search query parameters include one or more first entities Extracting one or more first entities from the partial search query parameters by identifying at least one entity type corresponding to the at least one entity type;
A fuzzy-score matching computer selects a fuzzy matching algorithm to search for an entity concurrency database to identify one or more records associated with partial search query parameters, the fuzzy matching algorithm corresponding to at least one identified entity type;
Wherein the fuzzy-score matching computer searches the entity concurrency database using the selected fuzzy matching algorithm and forms one or more of the presented first search query parameters from the one or more records based on the search;
The fuzzy-score matching computer providing one or more of the presented first search query parameters via a user interface;
The entity extraction computer receiving a user selection of one or more of the presented first search query parameters to form completed search query parameters;
The entity extraction computer extracts one or more second entities from the completed search query parameters;
Wherein the entity extraction computer searches for an entity concurrency database to identify one or more entities associated with one or more second entities to form one or more second presented search parameters;
Wherein the entity extraction computer comprises providing, via the user interface, one or more of the presented second search query parameters
Way.
39. The method of claim 37,
Wherein the fuzzy-score matching computer further comprises searching the entity co-occurrence database using the selected fuzzy matching algorithm before the user input is terminated
Way.
39. The method of claim 37,
The one or more records associated with the partial search query parameters may include conceptual features
Way.
39. The method of claim 37,
The one or more presented first search query parameters include a plurality of presented first search query parameters,
The method comprises:
Wherein the fuzzy-score matching computer further comprises sorting the plurality of presented first search query parameters in a downward order based on a proximity of a match for a partial search query parameter in a user input
Way.
41. The method of claim 40,
The fuzzy-score matching computer is configured to provide, via the user interface, a plurality of presented first search query parameters sorted into a drop-down list
Way.
39. The method of claim 37,
Entity Concurrency Database is Indexed
Way.
39. The method of claim 37,
Entity Concurrency A database contains an entity-to-entity index
Way.
39. The method of claim 37,
An entity-concurrency database is one that contains an entity-to-topic index
Way.
39. The method of claim 37,
An entity-concurrency database contains an entity-to-facts index
Way.
Comprising one or more server computers with one or more processor executable computer readable instructions for a plurality of computer modules,
The plurality of computer modules,
An entity extraction module;
A fuzzy-score matching module,
The entity extraction module,
The partial search query parameters from the user interface are configured to receive user input of the partial search query parameters having at least one incomplete search query parameter;
Compare the partial search query parameters with an entity concurrency database having instances of concurrent occurrence of one or more first entities in the electronic data corpus and compare at least two of the at least two first entities corresponding to the one or more first entities in the partial search query parameters The first entity being further configured to extract one or more first entities from the partial search query parameters by identifying one entity type,
The fuzzy-
The fuzzy matching algorithm is configured to identify one or more records associated with partial search query parameters by selecting a fuzzy matching algorithm that searches for an entity concurrent occurrence database, the fuzzy matching algorithm corresponding to at least one identified entity type;
Searching the entity concurrency database using the selected fuzzy matching algorithm, and forming one or more of the presented first search query parameters from the one or more records based on the search;
Further configured to provide one or more of the presented first search query parameters via a user interface,
Wherein the entity extraction module comprises:
Receiving a user selection of one or more of the presented first search query parameters to form a completed search query parameter;
Extract one or more second entities from the completed search query parameters;
Create one or more presented second search query parameters by searching the entity coincidence database to identify one or more entities associated with one or more second entities;
Further configured to provide one or more of the presented second search query parameters via the user interface
system.
47. The method of claim 46,
The fuzzy-score matching module is further configured to search the entity concurrency database using the selected fuzzy matching algorithm before the user input is terminated
system.
47. The method of claim 46,
The one or more records associated with the partial search query parameters may include conceptual features
system.
47. The method of claim 46,
The one or more presented first search query parameters include a plurality of presented first search query parameters,
The fuzzy-score matching module is further configured to sort the plurality of presented first search query parameters in a downward order based on a proximity of a match for a partial search query parameter in a user input
system.
50. The method of claim 49,
The fuzzy-score matching computer is configured to provide, via a user interface, a plurality of presented first search query parameters sorted into a drop-down list
system.
47. The method of claim 46,
Entity Concurrency Database is Indexed
system.
47. The method of claim 46,
Entity Concurrency A database contains an entity-to-entity index
system.
47. The method of claim 46,
An entity-concurrency database is one that contains an entity-to-topic index
system.
47. The method of claim 46,
An entity-concurrency database contains an entity-to-facts index
system.
As a computer implemented method,
The computer receiving a search query comprising one or more data strings from a search engine, each entity corresponding to a subset of one or more strings;
The computer identifying one or more entities in the one or more data strings based on comparing the entity database and the trend database with one or more entities;
The computer identifying one or more characteristics in one or more data strings that are not identified as corresponding to at least one entity;
The computer assigning each of the one or more features to at least one of the one or more entities based on a matching algorithm;
The computer assigning an extraction score to each entity based on a score assigned to each feature assigned to each entity;
The computer receiving a first search list from an entity database, the first search list including one or more entities having a score within a threshold distance from an extraction score of each entity;
The computer receiving a second search list from the trend database, the second search list including one or more entities having a score within a threshold distance from an extraction score of each entity;
The computer generating an aggregated list comprising a first search list and a second search list, the entities of the aggregation list being each ranked according to the aggregated scores;
Wherein the computer comprises providing a suggested search according to a collection list
Computer implemented method.
As a computer implemented method,
A computer receiving a plurality of data streams, each associated with a plurality of data sources;
The computer generating an array of characteristics associated with each of the data streams;
In response to the computer detecting a triggering condition associated with data in the data stream, the computer generating geographic data associated with data in the data stream;
Map an array of properties for a data source to a set of managed proportions associated with a search index, in response to the computer not detecting a triggering condition for a data source;
In response to determining that the content type of the data source is image data, the computer executes an optical character recognition routine on the metadata associated with the data received from the data source, and the computer identifies Retrieving from the web service an updated data stream from a data source,
The data source is associated with the web service identified by the metadata.
Computer implemented method.

Images