Entity Search Engine

Entity Search Engine

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  ENTITY SEARCH ENGINE: A NEW SEARCH TOOL Speaker : Tanmay Mondal , MSLIS 2013-2015 Indian Statistical Institute , Bangalore Documentation Research and Training Centre Seminar ( 1 ) - 2014
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  Present Approach ●Information is everywhere & it is growing exponentially ● A traditional information extraction approach is to scan every document in any collection ● As document collection is the set of all web pages indexed by a search engines ● Time consuming for users for getting pin-pointed information
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  Person Location Organization Nationality Religion Product For specific Information Phone Number Email Address/URL Distance Date Time Money Generic Number Problem of identifying and linking / grouping different manifestations of the same real world object
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  Web of DocumentsWeb of Entites Cluster the records that correspond to same entity
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  Entity Search ●Entity refers to any object or a thing that can be uniquely identified in the world ● It's a better match search queries with a database containing hundreds of millions of "entities" ● Each entity is in relation with many entites ● The answer entities have specific information & identifying the right relationship among the entities ● Semantic or faceted search on entities
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  Why ? ●When people use retrieval systems they are often not searching for documents or text passages ● Summarization of entities and concepts ● The named entities (persons, organizations, locations, products...) play a central role in answering such information needs ● At least 20-30% of the queries submitted to Web SE are simply entities ● ~71% of Web search queries contain named entities **Source - Building Taxonomy of Web Search Intents for Name Entity Queries by Xiaoxin Yin & Sarthak Shah
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  Benefit of EntitySearch ● Entities are often categorized into a taxonomy ● Primary task of the user is often to make a decision ● More structured than document based ● Entity is associated with the same URI across the different repositories ● Entity Information Integration ● More understandable by Human ● Increase precision & less Time Consuming
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  Entity & ItsFacets ● An entity must be distinguished from other entities Can be anything including an abstract thing like Diseases ,Imaginary art etc. ● Type of an entity refers to a generic class into which the given entity is classified. ● Attribute refers to a property (predicate) associated with an entity. ● Value refers to the value of an attribute (for a given entity). ● Relation provides more information with many entites ● Entity, Prof. S.R. Ranganathan is a person , IBM is an organization
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  Main Work ofESE ● Entity Retrieval : Entity search engines can return aranked list of entities most relevant for a user query ● Entity Relationship / Fact Mining and Navigation : It discover interesting relationships / facts about the entities associated with their queries ● Prominence Ranking : Detect the popularity of an entity and enable users to browse entities in different categories ● Entity Description Retrieval : Entity description blocks for each entity information about an object in a web page is generally grouped together as an object block
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  Popular Entity Search ● Product search-Various Products like Books, Electronics, Clothes, etc. ● People search-Experts, Friends, Profile of famous persons, etc. ● Location search-Travel, Address ,Business, Govt Offices, etc.
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  Idea about entitysearch engine
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  Main Work ofESE ● Entity Retrieval : Entity search engines can return aranked list of entities most relevant for a user query ● Entity Relationship / Fact Mining and Navigation : It discover interesting relationships / facts about the entities associated with their queries ● Prominence Ranking : Detect the popularity of an entity and enable users to browse entities in different categories ● Entity Description Retrieval : Entity description blocks for each entity information about an object in a web page is generally grouped together as an object block
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  Various ESE ●Freebase-http://www.freebase.com/ ● Sindice-http://sindice.com/ ●Geneview-http://bc3.informatik.hu-berlin.de/ ●Okkam-http://www.okkam.org/ ●WolframAlpha-http://www.wolframalpha.com/ ● Yatedo-http://www.yatedo.com/ ●GeoNames-http://www.geonames.org/ ●Dbpedia-http://dbpedia.org/About ● EntityCube-http:// entitycube.research.microsoft.com/ etc......
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  OKKAM-Enabling a Webof Entities ● Any collection of data and information about any type of entities published on the Web can be integrated into a single virtual, decentralized, open knowledge base. ● It leads to a faster, more efficient and more precise way to deal with the flood of information available on the Web today Entities should not be multiplied beyond necessity
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  OKKAM ENS ●OKKAM ENS is for entity search, where storage, indexing and matching technology was built for finding an entity given its description ● Every entity (individual, instance, “thing”) is assigned a global identifier, ideally unique ● More than 7.5 million entity repository with more structured form Entity identifiers should not be multiplied beyond necessity
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  Project Partners ●University of Trento, Italy (Co-Ordinator) ● L3S Research Center, Germany ● SAP Research, Germany ● Expert System, Italy ● Elsevier B.V., Netherlands ● Europe Unlimited SA, Belgium ● National Microelectronics Application Center (MAC), Ireland ● Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland ● DERI Galway, Ireland ● University of Malaga, Spain ● INMARK, Spain ● Agenzia Nazionale Stampa Associata (ANSA), Italy
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  Sources Of Information ● Wikipedia Provides lists of countries, cities, members of particulars domains which is very common for our search query ● GeoNames contains over 10 million geographical names and consists of over 9 million unique features of 2.8 million populated places and 5.5 million alternate names ● OkkamDBManager Another important information source for OKKAM can be generic databases like extranets, online shops or publishing houses ● OkkamManualEntry Another solution we provide to insert new entities is the manual case
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  Data extracted fromany unstructed sources more effectively
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  Cogito Semantic Technology ● Semantic analysis engine and complete semantic network for a complete understanding of text ● Transforming unstructured information into structured data ● Identifies the most relevant concepts ● Interprets the meaning of texts ● Precisely extracts information ● Automatically connet entites extracted from sources
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  Sensigrafo ● Enablesthe disambiguation of terms ● It allows Cogito to understand the meaning of words and context ● Extraction of data and metadata ● Product development, competitive intelligence,marketing ,Finance, Media & Publishing, Oil & Gas, Life Sciences & Pharma, Government and Telecommunications and many activities where knowledge sharing is critical ● More than 1 million concepts,more than 4 million relationships
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  WWoorrkkffllooww ooff OOkkkkaamm ● Storage: A scalable repository of entity profiles, in which billions of entities are assigned an ID and a profile, to distinguish one entity from another ● Matching: Requests from client applications arrive in the form of a bag of keywords or a collection of name value pairs (unstructured or semi-structured queries ● ID storage and management: stores, maintains and makes available for reuse IDs (URIs) for anything which is named in a networked environment ● Lifecycle Management: It takes care of the evolution Storage of the repository and of all entity profiles through different time
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  Entity Query &Matching in Okkam
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  ISI
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  Wolfram|Alpha ● Wolfram|Alphais an engine for computing answers and providing knowledge ● It generates output by doing computations from its own internal knowledge base, instead of searching the web and returning links ● It is an online service that answers factual queries directly by computing the answer ● Make all systematic knowledge immediately computable and accessible to everyone
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  5 nearest stars
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  How many newspapersare available in the globe
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  Overall Difficulties ●The number of entities could be huge ● Information Redundancy ● Information Fragmentation ● Entity Information Integration ● A single algorithm for fine­grained entity matching may not exist ● Store and retrieve using IR based techniques ● Matching on very large datasets ● Natural Language Processing
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  Contd... ● Availabilityof a knowledge base is less ● Multi‐domain entites ● Deduplication Problem ● Some names and relationships could be incorrect & the information may not be update­to­date ● Name disambiguation is still largely unsolved ● ESEs are at early age Creating knowledge bases from text and unstructured data is the goal
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  References 1. StatisticalEntity Extraction from Web by Zaiqing Nie, Ji-Rong Wen, and Wei-Ying Ma, Fellow, IEEE 2. State of the art in IE, overview, comparison and analysis by Stefan Dumitrescu ,PhD Student 3. The Entity Name System: Enabling the Web of Entities by Heiko Stoermer, Themis Palpanas, George Giannakopoulos,University of Trento 4. Hybrid entity clustering using crowds and data by Jongwuk Lee, Hyunsouk Cho,Jin-Woo Park,Young-rok Cha,Seung-won Hwang, Zaiqing Nie ,Ji-Rong Wen 5. Supporting Entity Search:A Large-Scale Prototype Search Engine by Tao Cheng, Xifeng Yan, Kevin Chen-Chuan Chang
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  References... 6. OKKAM:Enabling a Web of Entities by Paolo Bouquet ,Heiko Stoermer ,Daniel Giacomuzzi ,University of Trento 7. Entity Data Management in OKKAM by Themis Palpanas 1 , Junaid Chaudhry 2 , Periklis Andritsos 1 , Yannis Velegrakis 1 ,1 University of Trento,2 Ajou University 8. SPACE AND TIME ENTITY REPOSITORY Human-enhanced time-awaremulti media search funded by EU07 See :http://issuu.com/cubrikproject/docs/issuu.cubrik.d41.unitn.wp4.v1.0 9. http://api.okkam.org/search/ 10. http://www.wolframalpha.com/