WO2015084759A1 - Systèmes et procédés permettant une recherche de base de données en mémoire - Google Patents

Systèmes et procédés permettant une recherche de base de données en mémoire Download PDF

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Publication number
WO2015084759A1
WO2015084759A1 PCT/US2014/067997 US2014067997W WO2015084759A1 WO 2015084759 A1 WO2015084759 A1 WO 2015084759A1 US 2014067997 W US2014067997 W US 2014067997W WO 2015084759 A1 WO2015084759 A1 WO 2015084759A1
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WO
WIPO (PCT)
Prior art keywords
entity
entities
search
computer
search query
Prior art date
Application number
PCT/US2014/067997
Other languages
English (en)
Inventor
Scott Lightner
Franz Weckesser
Rakesh Dave
Sanjay Boddhu
Joseph Becknell
Birali HAKIZUMWAMI
Original Assignee
Qbase, LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Qbase, LLC filed Critical Qbase, LLC
Priority to CN201480072953.7A priority Critical patent/CN106164889A/zh
Priority to JP2016536900A priority patent/JP2017504105A/ja
Priority to KR1020167017516A priority patent/KR20160124079A/ko
Priority to EP14867913.7A priority patent/EP3077918A4/fr
Priority to CA2932401A priority patent/CA2932401A1/fr
Publication of WO2015084759A1 publication Critical patent/WO2015084759A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/31—Indexing; Data structures therefor; Storage structures
    • G06F16/313—Selection or weighting of terms for indexing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3322—Query formulation using system suggestions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/3332—Query translation
    • G06F16/3334—Selection or weighting of terms from queries, including natural language queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic

Definitions

  • the present disclosure relates generally to methods and systems for information retrieval; more specifically, a method for searching for related entities using entity co-occurrence.
  • the present disclosure relates generally to query enhancement; more specifically, search suggestions using fuzzy-score matching and entity co-occurrence in a knowledge base.
  • the present disclosure relates generally to computer query processing; more specifically, electronic search suggestions of related entities based on co-occurrence and/or fuzzy score matching.
  • the present disclosure relates generally to methods and systems for information retrieval; more specifically, a method for obtaining search suggestions.
  • the present disclosure generally relates to search engines and content management; more specifically, extending a content management system's search engine technology to enable geotagging and named entities enrichment of digital content.
  • search engines for locating information of interest either from the Internet or any database system.
  • Search engines commonly operate by receiving a search query from a user and returning search results to the user. Search results are usually ordered based on the relevance of each returned search result to the search query. Therefore, the quality of the search query may be significantly important for the quality of search results.
  • search queries from users in most cases, may be written incomplete or partial (e.g., the search query may not include enough words to generate a focused set of relevant results and instead generates a large number of irrelevant results), and sometimes misspelled (e.g., Bill Smith may be incorrectly spelled as "Bill Smitth").
  • One common approach to improve the quality of the search results is to enhance the search query.
  • One way to enhance the search query may be by generating possible suggestions based on the user's input.
  • some approaches propose methods for identifying candidate query refinements for a given query from past queries submitted by one or more users.
  • these approaches are based on query logs that sometimes may lead the user to results that may not be of interest.
  • Search engines include a plurality of features in order to provide a forecast for user's query. Such forecast may include query auto-complete and search suggestions.
  • forecast methods are based on historic keywords references. Such historic references may not be accurate because one keyword could be referred to a plurality of topics in a single text.
  • SharePoint 2013® is a family of software products developed by Microsoft Corporation for collaboration, file sharing and web publishing.
  • SharePoint 2013® may provide a user with a vast amount of content or information and it may become difficult for a user to find the most relevant information for a particular circumstance.
  • SharePoint 2013® provides a search engine in order to assist users in finding the content that they need. A user may enter a keyword based search query and the search engine in SharePoint 2013® may return to the user a list of the most relevant results found within the context of the SharePoint 2013® platform once the content has been indexed.
  • SharePoint 2013® or other type of entity such as organizations or people referred to within a document.
  • SharePoint 2013® does not provide out of the box functionality to automatically extract entities from documents. Particularly, it does not support geotagging content to extract geographic entities and resolve them to a geographic location. Also, SharePoint 2013 does not support entity tagging in order to identify, disambiguate and extract named entities, such as, organizations or people in a document.
  • SharePoint 2013® search may be extended to enable effective geographic searches and other entity related searches, including entity-based search facets.
  • Previous versions of SharePoint 2013® included "FAST Search" for SharePoint, from which it was possible to extend the content processing pipeline through sandboxed applications, but this was both slow and limited in the information it could access.
  • SharePoint 2013® introduces a much more open API which makes it possible to add specialized linguistics such as concept extraction, relationship extraction, geotagging, summarization and as well as sophisticated text analytics. Thus, an opportunity exists to extend the capabilities of SharePoint 2013® search engine to enable geographic and other entity based searches.
  • a method for searching for related entities using entity co-occurrence may be employed in a search system that may include a client/server type architecture.
  • the search system may include a user interface for a search engine in communication with one or more server devices over 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 built as an in-memory database and may also include other components such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation module.
  • One search controller may be selectively associated with one or more search nodes. Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.
  • the method further includes searching, by the fuzzy-score matching computer, the entity co-occurrence 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, and presenting, by the fuzzy-score matching computer, the one or more suggested search query parameters via the user interface.
  • the completed search query may be used as a new search query.
  • the search system may process the new search query, run an entity extraction, find related entities with the highest scores from the entity co-occurrence knowledge base, and present said related entities in a drop down list that may be useful for the user.
  • a system comprising one or more server computers having one or more processors executing computer readable instructions for a plurality of computer modules including an entity extraction module configured to receive user input of partial search query parameters from a user interface, the partial search query parameters having at least one incomplete search query parameter, the entity extraction module being further configured to extract one or more first entities from the partial search query parameters by comparing the partial search query parameters with an entity co-occurrence database having instances of co-occurrence of the one or more first entities in an electronic data corpus and identifying at least one entity type corresponding to the one or more first entities in the partial search query parameters.
  • a computer-implemented method comprises receiving, by a computer, from a search engine a search query comprising one or more strings of data, wherein each respective entity corresponds to a subset of the one or more strings; identifying, by the computer, one or more entities in the one or more strings of data based on comparing the one or more entities against an entity database and a trends database; identifying, by the computer, one or more features in the one or more strings of data not identified as corresponding to at least one entity; assigning, by the computer, each of the one or more features to at least one of the one or more entities based on a matching algorithm; assigning, by the computer, an extraction score to each respective entity based on a score assigned to each respective feature assigned to the respective entity; receiving, by the computer, from an entity database a first search list containing one or more entities having a score within a threshold distance from the extraction score of each respective entity; receiving, by the computer, from a trends database a second search list containing one or more entities having
  • FIG. 1 is a block diagram illustrating an exemplary environment of a computer system in which one embodiment of the present disclosure may operate;
  • FIG. 9 is an example embodiment of a user interface associated with the method described in FIG. 8.
  • FIG. 10 is a block diagram illustrating a method for obtaining search suggestions based on entities and trends databases.
  • FIG. 12 is a block diagram illustrating a method for obtaining search suggestions based on entities and trends databases, by generating a list of suggestions based on an overall score of search suggestions on both databases.
  • FIG. 13 is a system architecture for tagging and entity enrichment of content in a content management system.
  • FIG. 14 is a process by which content is tagged and indexed for named and geographic entity searches.
  • Features is any information which is at least partially derived from a document.
  • Event Concept Store refers to a database of Event template models.
  • Event Model refers to a collection of data that may be used to compare against and identify a specific type of event.
  • Feature attribute refers to metadata associated with a feature; for example, location of a feature in a document, confidence score, among others.
  • Query refers to a computer generated request to retrieve information from one or more suitable databases.
  • Geotagging refers to the process of extracting geographic entities from unstructured text files. Geotagging may include disambiguating the entity to a specific geographic place and appending geographic metadata such as geographic coordinates, geographic feature type and other metadata.
  • Named Entity refers to a person, organization or topic.
  • Geographic Entity refers to geographic location or geographic places.
  • “Crawled Properties” refers to content management system metadata obtained from inspecting documents during crawls.
  • FIG. 1 is a block diagram of a search system 100 in accordance with the present disclosure.
  • the search system 100 may include one or more client computing device comprising a processor executing software modules associated with the search system 100, which may include graphical user interfaces 102 accessing a search engine 104 communicating search queries in the form of binary data with a server device 106, over a network 108.
  • the search system 100 may be implemented in a client-server computing architecture.
  • the search system 100 may be implemented using 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, and the like).
  • ASP application service provider
  • a user's computing device 102 may access a search engine 104, which may include software modules capable of transmitting search queries.
  • Search queries are parameters provided to the search engine 104 indicating the desired information to retrieve.
  • Search queries may be provided by a user or another software application in any suitable data format (e.g., integers, strings, complex objects) compatible with the search engine's 104 parsing and processing routines.
  • the search engine 104 may be a web- based tool that is accessible through the user's computing device 102 browser or other software application, and enables users or software applications to locate information on the World Wide Web.
  • the search engine 104 may be application software modules native to the system 100, enabling users or applications to locate information within databases of the system 100.
  • Server device 106 which may be implemented as a single server device 106 or in a distributed architecture across a plurality of server computers, may include an entity extraction module 110, an entity co-occurrence knowledge base 112, and an entity indexed corpus 114.
  • Entity extraction module 110 may be a computer software and/or hardware module able to extract and disambiguate independent entities from a given set of queries such as a query string, structured data and the like.
  • Example of entities may include people, organizations, geographic locations, dates and/or time.
  • feature recognition and extraction algorithms may be employed.
  • a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.
  • entity co-occurrence knowledge base 112 may be built, but is not limited to, as an in-memory computer database (not shown) and may include other components (not shown), such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation computer module.
  • One search controller may be selectively associated with one or more search nodes.
  • Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.
  • Entity co-occurrence knowledge base 112 may include related entities based on features and ranked by a confidence score. Various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed.
  • Entity indexed corpus 114 may include data from a plurality of sources such as the Internet having a massive corpus or live corpus.
  • the entity extraction module 110 may process search queries from step 202 as entities and compare them all against entity co-occurrence knowledge base 112 to extract and disambiguate as many entities as possible.
  • one or more feature recognition and extraction algorithms may be employed.
  • a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes.
  • the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.
  • various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed.
  • an index ID which in some cases may be a number, may be assigned in step 206 to the extracted entities.
  • a search based on the entities index ID assigned in step 206 may be performed.
  • the extracted entities may be located within the entity indexed corpus 114 data by using standard indexing methods.
  • an entity association step 210 may follow.
  • all the data such as documents, videos, pictures, files or the like, where at least two extracted entities overlaps may be pulled from the entity indexed corpus 114.
  • a list of potential results is built, sorted by relevance, and presented to the user as search results, step 212. The list of results may then show only links to data where the user may find related entities of interest.
  • a user may be looking for information regarding
  • the user may input one or more entities (e.g., search queries in step 302) through a user interface 102 which may be, but is not limited to, an interface with a search engine 104, such as the one described in FIG. 1.
  • entities e.g., search queries in step 302
  • the search engine 104 may generate search queries, step 302, and send these queries to server device 106 to be processed.
  • entity extraction module 110 may perform an entity extraction step 304 from search queries input in step 302.
  • a table 306 including entity and co-occurrences may be created.
  • Table 306 may then show the entity "apple" and its co-occurrences, which in this case, may be Apple and Jobs, Apple and Steve Jobs.
  • the table 306 may also include Apple and organization A which may have been found relevant because Organization A is doing business with Apple and generating "jobs" in said organization A. Other co-occurrences may be found with less importance.
  • Apple and Jobs may then have the highest score (1), thus listed at the top, then Apple and Steve Jobs may have the second highest score (0.8), and finally Apple and other organization A may be at the bottom list with the lowest score (0.3).
  • a search step 312 based on the entities index ID 308 may be performed.
  • FIG. 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 to a search engine 404 in communication with a server device 406 over a network 408.
  • the search system 400 may be implemented in one or more special purpose computers and computer modules referenced below, including via a client/server type architecture.
  • the search system 400 may be implemented using other computer architectures (for example, a stand-alone computer, a mainframe system with terminals, an ASP model, a peer to peer model and the like).
  • the search computer system 400 includes a plurality of networks such as, a local area network, a wide area network, the internet, a wireless network, a mobile phone network and the like.
  • a search engine 404 may include a user interface, such as a web-based tool that enables users to locate information on the World Wide Web. Search engine 404 may also include user interface tools that enable users to locate information within internal database systems.
  • Server device 406 which may be implemented in a single server device 406 or in a distributed architecture across a plurality of server computers, may include an entity extraction module 410, a fuzzy-score matching module 412, and an entity co-occurrence knowledge base database 414.
  • Entity extraction module 410 may be a hardware and/or software module configured to extract and disambiguate on-the-fly independent entities from a given set of queries such as a query string, partial query, structured data and the like. Examples of entities may include people, organizations, geographic locations, dates and/or time.
  • entities may include people, organizations, geographic locations, dates and/or time.
  • one or more feature recognition and extraction algorithms may be employed.
  • a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.
  • entity co-occurrence knowledge base 414 may be built, but is not limited to, as an in-memory database and may include components, such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation module.
  • One search controller may be selectively associated with one or more search nodes.
  • Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.
  • FIG. 5 is a flowchart illustrating a method 500 for generating search suggestions using fuzzy-score matching and entity co-occurrence in a knowledge base.
  • Method 500 may be implemented in a search system 400, similar to that described by FIG. 4.
  • method 500 may initiate when a user starts typing a search query in step 502 into a search engine interface 402, as described in FIG. 4.
  • search system 400 may perform an on-the-fly process.
  • search query input in step 502 may be either complete or partial, either correctly spelled or misspelled.
  • a partial entity extraction step 504 from the search query input of step 502 may be performed.
  • the partial entity extraction step 504 may run a quick search against entity co-occurrence knowledge base 414 to identify whether the search query that was input in step 502 is an entity, and if so, what type of entity it is.
  • search query input of step 402 may then refer to a person, an organization, the location of a place, and a date among others.
  • fuzzy-score matching module 412 may select a corresponding fuzzy matching algorithm, step 506. For example, if search query was identified as an entity that is referring to a person, then fuzzy-score matching module 412 may select the string matching algorithm for persons, for example, such as by extracting different components of the person's name including first, middle, last, and title.
  • fuzzy-score matching module 412 may select the string matching algorithm for organizations, which can include identifying terms like school, university, corp, inc, and the like. Fuzzy-score matching module 412 may then select the string matching algorithm that corresponds to the type of identified entity in the search query input so as to excel the search. Once the string matching algorithm is adjusted to the type of identified entity, a fuzzy-score matching step 508 may be performed.
  • fuzzy-score matching step 508 finishes comparing and searching search query against all records in the entity co-occurrence knowledge base 414, the record that dominates the most or is the closest to match the given pattern string (i.e., the search query input of step 502) may be selected as first candidate for a search suggestion in step 510.
  • Other records with less proximity to match the given pattern string may be placed under the first candidate in a descending order.
  • Search suggestion in step 510 may then be presented to the user in a drop down list of possible matches that the user may or may not ignore.
  • FIG. 6 is an example user interface 600 in accordance with the method for generating search suggestions using fuzzy-score matching and entity co-occurrence in a knowledge base, as discussed in FIGS 4-5 above.
  • a user through a search engine interface 602, similar to that described by FIG. 4, inputs a partial query 604 in a search box 606.
  • partial query 604 may be an incomplete name of a person such as "Michael J", as shown in FIG. 6. It may be considered a partial query 604 because the user may not have yet selected search button 608, or otherwise submitted the partial query 604 to search system 400 to perform an actual search and obtain results.
  • Fuzzy-score matching module 412 may use a common string metric such as Levenshtein distance to determine and assign a score to the entity, topic, or fact within entity co-occurrence knowledge base 414 that may match the entity "Michael".
  • Levenshtein distance may be used to determine and assign a score to the entity, topic, or fact within entity co-occurrence knowledge base 414 that may match the entity "Michael".
  • Michael matches with a great amount of records having that name.
  • fuzzy-score matching module 412 may perform another comparison based on Levenshtein distance against all co-occurrences with Michael with the entity co-occurrence knowledge base 414. Entity co-occurrence knowledge base 414 may then select all possible matches with the highest scores for "Michael J".
  • fuzzy-score matching module 412 may return search suggestions 610 such as "Michael Jackson”, “Michael Jordan”, “Michael J. Fox", or even “Michael Dell” in some cases to the user. The user may then be able to select from the drop down list one of the persons suggested as to complete the search query. Expanding on the aforementioned example, a query like “Michael the basketball player”, would lead to the suggestion of "Michael Jordan", based on the results returned by searching entity co-occurrence knowledge base for "Michael” in person entity name variations and "the basketball player” in the co-occurrence features like key phrases, facts, and topics. Another example can be “Alexander the actor”, would lead to the suggestion of "Alexander Polinsky”. Those skilled in the art will realize that the presently existing search platforms cannot generate suggestions in the aforementioned manner.
  • FIG. 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 to a search engine 704 in communication with a server device 706 over a network 708.
  • the search system 700 may be implemented in a client/server type architecture; however, the search system 700 may be implemented using other computer architectures (for example, a stand-alone computer, a mainframe system with terminals, an ASP model, a peer to peer model and the like) and a plurality of networks such as, a local area network, a wide area network, the internet, a wireless network, a mobile phone network and the like.
  • a search engine 704 may include, but is not limited to, an interface via a web- based tool that enables users to locate information on the World Wide Web. Search engine 704 may also include tools that enable users to locate information within internal database systems.
  • Server device 706, which may be implemented in a single server device 706 or in a distributed architecture across a plurality of server computers, may include an entity extraction module 710, a fuzzy-score matching module 712, and an entity co-occurrence knowledge base database 714.
  • Entity extraction module 710 may be a hardware and/or software computer module able to extract and disambiguate on-the-fly independent entities from a given set of queries such as a query string, partial query, structured data and the like.
  • Example of entities may include people, organizations, geographic locations, dates and/or time.
  • one or more feature recognition and extraction algorithms may be employed.
  • a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.
  • entity co-occurrence knowledge base 714 may be built, but is not limited to, as an in-memory database and may include components such as one or more search controllers, multiple search nodes, collections of compressed data, and a disambiguation module.
  • One search controller may be selectively associated with one or more search nodes.
  • Each search node may be capable of independently performing a fuzzy key search through a collection of compressed data and returning a set of scored results to its associated search controller.
  • Entity co-occurrence knowledge base 714 may include related entities based on features and ranked by a confidence score.
  • Various methods for linking the features may be employed, which may essentially use a weighted model for determining which entity types are most important, which have more weight, and, based on confidence scores, determine how confident the extraction of the correct features has been performed.
  • FIG. 8 is a flowchart illustrating an embodiment of a method 800 for generating search suggestions of related entities based on co-occurrence and/or fuzzy score matching.
  • Method 800 may be implemented in a search system 700, similar to as described in FIG. 7.
  • fuzzy-score matching step 808 extracted entity or entities, as well as any non-entities, may be searched and compared against entity co-occurrence knowledge base 714.
  • Extracted entity or entities may include incomplete names of persons, for example first name and the first character of the last name, abbreviations of organizations, for example "UN” that may stand for "United Nations", short forms, and nicknames among others.
  • Entity co-occurrence knowledge base 714 may already have registered a plurality of records indexed in an structured data, such as entity to entity, entity to topics, and entity to facts index among others. This may allow fuzzy-score matching in step 808 to happen expeditiously.
  • Fuzzy- score matching may use, but is not limited to, a common string metric such as Levenshtein distance, strcmp95, ITF scoring, and the like.
  • Levenshtein distance between two words may refer to the minimum number of single-character edits required to change one word into the other.
  • search system 700 may take that selection as a new search query, step 812. Subsequently, an entity extraction step 814 from said new search query may be performed. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model. Entity extraction module 710 may then run a search against entity co-occurrence knowledge base 714 to find related entities, step 816, based on the cooccurrences with the highest scores.
  • FIG. 9 is an example embodiment of a user interface 900 associated with the method 800 for generating search suggestions of related entities based on co-occurrence and/or fuzzy score matching.
  • a user through a search engine interface 902, similar to that described by FIG. 7, inputs a partial query 904 in a search box 906.
  • partial query 304 may be an incomplete name of a person such as "Michael J", as shown in FIG. 9. It may be considered a partial query 904 because the user may not have yet selected search button 908, or otherwise submitted the partial query 904 to search system 100 to perform an actual search and obtain results.
  • Fuzzy-score matching module 712 may use a common string metric such as Levenshtein distance to determine and assign a score to the entity, topic, or fact within entity co-occurrence knowledge base 714 that may match the entity "Michael". In this example, Michael matches with a great amount of records having that name. However, as the user types the following character "J", fuzzy-score matching module 712 may perform another comparison based on Levenshtein distance against all co-occurrences with Michael with the entity co-occurrence knowledge base 714. Entity co-occurrence knowledge base 714 may then select all possible matches with the highest scores for "Michael J".
  • a common string metric such as Levenshtein distance
  • the user may select "Michael Jordan" from the drop down list to complete the partial query 904, as indicated in FIG. 9. Said selection may then be processed as a new search query 912 by search system 700. Subsequently, an entity extraction from said new search query 912 may be performed. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.
  • FIG. 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, such search engine 1002 may include one or more user interfaces allowing data input from the user, such as user queries.
  • a search engine 1002 may include, but is not limited to, a web-based tool that enables users to locate information on the World Wide Web. Search engine 1002 may also include tools that enable users to locate information within internal database systems.
  • Entity database 1004 which may be implemented as a single server or in a distributed architecture across a plurality of servers. Entity database 1004 may allow a set of entities queries, such as a query string, structured data and the like. Such set of entities queries may be previously extracted from a plurality of corpus available in the internet and/or local network. Entities queries may be indexed and scored. Example of entities may include people, organizations, geographic locations, dates and/or time. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.
  • Trends database 1006 which may be implemented as a single server or in a distributed architecture across a plurality of servers. Trends database 1006 may allow a set of entities queries, such as a query string, structured data, and the like. Such set of entities queries may be previously extracted from historical queries performed by the user and/or a plurality of users in the internet and/or local network. Entities queries may be indexed and scored. Example of entities may include people, organizations, geographic locations, dates and/or time. During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.
  • entities queries such as a query string, structured data, and the like.
  • Entities queries may be indexed and scored. Example of entities may include people, organizations, geographic locations, dates and/or
  • FIG. 11 is a block diagram of a search system 1100 in accordance with the present disclosure.
  • the search system 1100 may include a search engine 1102, such search engine 1102 may include one or more user interfaces allowing data input from the user, such as user queries.
  • search system 1100 may start when a user inputs one or more entities (in search queries) through a user interface in search engine 1102.
  • An example of a search query may be a combination of keywords in a string data format, structured data, and the like. These keywords may be entities that represent people, organizations, geographic locations, dates and/or time. In the present embodiment, "Indiana Na" is used as search query.
  • An entity extraction module may process search queries such as, "Indiana Na” as entities and compare them all against entity co-occurrence knowledge base in entity database 1104 and trends database 1106 to extract and disambiguate as many entities as possible. Additionally, the query text parts that are not detected as entities (e.g., person, organization, location), are treated as conceptual features (e.g., topics, facts, key phrases) that can be employed for searching the entity co-occurrence knowledge bases (e.g., entity and trend databases). During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Taking into account the feature attributes, the relative weight or relevance of each of the features may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.
  • search queries such as, "Indiana Na” as entities and compare them all against entity co-occurrence knowledge base in entity database 1104 and trends database 1106 to extract and disambiguate as many entities as
  • entity database 1104 may show a list of search suggestions, as a list of entity suggestions 1108, which may be indexed and ranked.
  • Trends database 1106 may show a list of search suggestions, as trends based suggestion list 1110, which may be indexed and ranked.
  • search system 1100 may build a search suggestions list 1112 based on those provided by entity database 1104 and trends database 1106.
  • the search suggestions list 1112 may be indexed and ranked based on the individual score of each entity suggestion in each database; thus, the most relevant may be shown first and the less relevant result may continue below it.
  • Search suggestions list 1112 may show suggestions based on "Indiana Na" user query. As a result, "Indiana Name” may appear first based on an individual score of 0.9 for that entity, then "Indiana Nascar” may be shown as a result of an individual score of 0.8, finally "Indiana Arlington” may be shown based on an individual score of 0.7. The individual score may be compared using list of entity suggestions 1108 and trends based suggestion list 1110, without applying considering repeated entities.
  • FIG. 12 is a block diagram of a search system 1200 in accordance with the present disclosure.
  • Search system 1200 may include a search engine 1202, such search engine 1202 may include one or more user interfaces allowing data input from the user, such as user queries.
  • Search system 1200 may include one or more databases. Such databases may include entity database 1204 and trends database 1206. Databases may be stored in a local server or in a web based server. Thus, search system 1200 may be implemented in a client/server type architecture; however, the search system 1200 may be implemented using other computer architectures; for example, a stand-alone computer, a mainframe system with terminals, an ASP model, a peer to peer model, and the like, and a plurality of networks such as, a local area network, a wide area network, the internet, a wireless network, a mobile phone network, and the like.
  • An entity extraction module may process search queries such as, "Indiana Na,” as entities and compare them all against entity co-occurrence knowledge base in entity database 1204 and trends database 1206 to extract and disambiguate as many entities as possible. Additionally, the query text parts that are not detected as entities (e.g., person, organization, location), are treated as conceptual features (e.g., topics, facts, key phrases), which may be employed for searching the entity co-occurrence knowledge bases (e.g., entity database, trend databases). During the extraction, one or more feature recognition and extraction algorithms may be employed. Also, a score may be assigned to each extracted feature, indicating the level of certainty of the feature being correctly extracted with the correct attributes. Based on the respective feature attributes, the relative weight and/or the relevance of each of the features, may be determined. Additionally, the relevance of the association between features may be determined using a weighted scoring model.
  • FIG. 13 is a system architecture 1300 for geotagging content in SharePoint
  • a Search index 1324 is one of a number of key components in order to enable search in SharePoint 1302. Another key part of enabling search in SharePoint 2013® 1302 may be content capturing in order to index the content.
  • SharePoint 1302 includes a crawler 1304 component in order to enable content capturing.
  • Content processing 1308 may be extended by means of a content enrichment web service (CEWS 1310).
  • CEWS 1310 may enable the enrichment of content processing 1308 by allowing a web service callout 1312 to call external web service to perform additional actions and enrich the crawled data properties.
  • Web service callout 1312 may be a standard simple object access protocol (SOAP) request or any other web service call method used to exchange structured information of the crawled data with an entity enrichment service 1314.
  • Web service callout 1312 may include trigger conditions configured in the content enrichment configuration object that control when to call an external web service for enrichment processing.
  • Entity enrichment service 1314 may also determine the document type of the crawled data in order to determine content that may come in the form of an image (scanned documents, pictures, etc.).
  • the entity enrichment service 1314 may send the location of the crawled document to an OCR processing engine 1316 such as for example and without limitation an optical character recognition component or other image processing component.
  • OCR processing engine 1316 may then retrieve and process the image files and convert them to text files asynchronously.
  • the OCR'd processed files 1318 may subsequently be re-fed to crawler 1304 in order to be crawled as text files and sent back to content processing 1308 and proceed with the rest of the workflow.
  • Geotagger web service 1320 may analyze an array of managed properties sent as input properties by CEWS 1310 and identify any geographic entities referred in text.
  • input properties may include: FileType, IsDocument, OriginalPath and body among others.
  • Geotagger web service 1320 may then geotag the text by creating or modifying managed properties with reference to each geographic entity found.
  • Geotagger web service 1320 may send modified or new managed properties to the entity enrichment service 1314 where a conversion is made that maps the modified managed properties and returns them as output properties back to CEWS 1310.
  • the same process may be used to interact with the named entity tagger service 1322 for the extraction and entity tagging of other entities or text features such as organizations, people or topics.
  • FIG. 14 is a flow chart 1400 illustrating the process steps for tagging content for SharePoint 2013® search.
  • the process may begin when the crawler component in SharePoint 2013® performs a crawl for content (step 1402).
  • the crawl may be a full crawl, wherein in another embodiment the crawl may be an incremental crawl.
  • the crawler component may then feed crawled properties and metadata to the content processing (step 1404).
  • a determination is made to verify if the crawled content may include geographic or named entities.
  • a trigger condition may be used.
  • the trigger condition may contain a set of programmatic logic or rules which may determine if content may benefit from geotagging or entity tagging.
  • the web service may extract and disambiguate geographic or named entities referred in the content and enrich them with entity metadata.
  • the identified entities and their metadata may be sent back as managed properties to the content processing component and associated with the content (step 1416).
  • the associated metadata may then be sent to the search index component (step 1406).
  • process flow diagrams are provided merely as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. As will be appreciated by one of skill in the art the steps in the foregoing embodiments may be performed in any order. Words such as “then,” “next,” etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods.
  • process flow diagrams may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged.
  • a process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function.
  • Embodiments implemented in computer software may be implemented in software, firmware, middleware, microcode, hardware description languages, or any combination thereof.
  • a code segment or machine-executable instructions 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 a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents.
  • Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
  • the functions may be stored as one or more instructions or code on a non-transitory computer-readable or processor-readable storage medium.
  • the steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module which may reside on a computer-readable or processor-readable storage medium.
  • a non-transitory computer-readable or processor- readable media includes both computer storage media and tangible storage media that facilitate transfer of a computer program from one place to another.
  • a non-transitory processor-readable storage media may be any available media that may be accessed by a computer.
  • non-transitory processor-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other tangible storage medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer or processor.
  • Disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
  • the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a non- transitory processor-readable medium and/or computer-readable medium, which may be incorporated into a computer program product.
  • the various links connecting the elements can be wired or wireless links, or any combination thereof, or any other known or later developed element(s) that is capable of supplying and/or communicating data to and from the connected elements.
  • module as used herein can refer to any known or later developed hardware, software, firmware, or combination thereof that is capable of performing the functionality associated with that element.
  • determine, calculate and compute, and variations thereof, as used herein are used interchangeably and include any type of methodology, process, mathematical operation or technique.

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Abstract

La présente invention concerne des systèmes et des procédés permettant d'identifier des entités associées à l'aide d'une base de connaissances de co-occurrence d'entités. Les modes de réalisation extraient des entités identifiées dans des requêtes de recherche à l'aide d'une base de connaissances de co-occurrence d'entités des entités extraites à partir d'un corpus indexé d'entités pour présenter des résultats de recherche en tant qu'entités associées. La présente invention concerne également des modes de réalisation permettant de générer des suggestions de recherche par l'utilisation d'un résultat flou correspondant à une base de connaissances de co-occurrence d'entités. Les modes de réalisation extraient des entités partielles à partir des requêtes de recherche, exécutent des algorithmes de correspondance basés sur des types d'entités extraites, et effectuent des recherches par rapport à la base de connaissances de co-occurrence d'entités. L'invention concerne également des modes de réalisation permettant de générer des suggestions de recherche d'entités associées basées sur une correspondance de résultats flous et/ou de co-occurrence. Les modes de réalisation procèdent à des requêtes de recherches partielles et présentent des suggestions de requêtes complètes, qui sont utilisées en tant que nouvelles requêtes de recherche. L'invention concerne également des modes de réalisation permettant de générer des suggestions de recherche par l'utilisation d'une co-occurrence d'entités grâce à l'extraction d'entités à partir de requêtes de recherches à l'aide d'une base de connaissances de co-occurrence de tendances et d'entités. L'invention concerne également des modes de réalisation permettant à des recherches géographiques basées sur une entité nommée de rechercher des capacités dans des systèmes de gestion de satisfaction.
PCT/US2014/067997 2013-12-02 2014-12-02 Systèmes et procédés permettant une recherche de base de données en mémoire WO2015084759A1 (fr)

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