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Toward Semantic Web Information Extraction

Toward Semantic Web Information Extraction. B. Popov, A. Kiryakov, D. Manov, A. Kirilov, D. Ognyanoff, M. Goranov Presenter: Yihong Ding. Toward a Semantic Web. Fully automatic methods for the semantic annotation are needed Related topics Information retrieval (IR)

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Toward Semantic Web Information Extraction

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  1. Toward Semantic Web Information Extraction B. Popov, A. Kiryakov, D. Manov, A. Kirilov, D. Ognyanoff, M. Goranov Presenter: Yihong Ding

  2. Toward a Semantic Web • Fully automatic methods for the semantic annotation are needed • Related topics • Information retrieval (IR) • Information extraction (IE) • Name-entity recognition (NER) • Annotation processes

  3. Semantic Annotation Diagram

  4. Name Entities • Named Entities (NE) • people, organizations, locations, and others referred by name. • May also include scalars and expressions • numbers, amounts of money, dates, etc. (NUMEX, TIMEX) • Hypothesis • Named entities (and the relations between them) mentioned in a resource constitute an important part of its semantics

  5. Semantic Annotation of NEs • Semantic Annotation of the NEs in a text includes: • Recognition of the type of the entities in the text • Identification of the entity individual • Comparison • the traditional NER approach results in: <Person>Yihong Ding</Person> • the Semantic Annotation of NEs should result in something like the following: <BYUPerson ID=“http://..byu../YihongDing”>Yihong Ding</BYUPerson>

  6. The KIM Platform • The Knowledge and Information Management Platform provides: • Automatic Semantic Annotation of NEs (and relations between them) • Ontology Population with NE individuals and relations • Indexing and Retrieval w.r.t NEs • Query and Navigation over the Formal Knowledge

  7. KIM Constituents • KIM Ontology (KIMO) • KIM World KB • KIM Server – with API for remote access and integration • Front-ends: KIM Web UI, Plug-in for Internet Explorer, and KB Explorer

  8. KIM Bases • KIM is based on the following open-source platforms: • GATE – NLP and IE platform in University of Sheffield • Sesame – RDF(S) repository Administrator b.v. • Ontology Middleware and Custom Inference by Ontotext as extensions of Sesame • Lucene – open source IR-engine from Apache

  9. KIM Architecture

  10. KIM Ontology (KIMO) • Light-weight upper-level ontology • 250 NE classes • 100 relations and attributes: • covers mostly NE classes, and ignores general concepts • includes classes representing lexical resources • www.ontotext.com/KIM/kimo.rdfs

  11. KIM World KB • A projection of the world (domain ontology) • Quasi-exhaustive coverage of the most popular entities in the world • Entities of general importance – like the ones that appear in the news • At present KIM KB consists of about 200,000 entities: • 50,000 locations, 130,000 organizations, 6000 people, etc.

  12. Entity Description • NEs are represented in KIM World KB with their Semantic Descriptions consisting of… • Aliases (Florida & FL) • Relations with other entities (Person hasPosition Position) • Attributes (latitude & longitude of geographic entities) • Proper class of the NE

  13. KIM Server • APIs for: • Semantic Annotation • Document Persistence • Indexing & Retrieval of documents w.r.t NEs • Semantic Repository Access & Exploration

  14. KIM Semantic Information Extraction • Based on GATE • NLP IE platform • Rules now based on ontology classes instead of a flat set of NE types • Recognition and Identification of the NEs • IE supported by a Semantic Repository • Containing lexical and gazetteer resources • Annotations referring to Entity Descriptions • Ontology Population with the newly recognized entities & relations

  15. KIM IE Pipeline

  16. KIM Plug-in

  17. KIM IE Performance • Evaluated over 3 human-annotated corpora of news articles: • International Business News, International Political News, and UK Political News (~500 articles): • Precision 86%, Recall 84% w.r.t the standard NE types • But these metrics are not representative for semantic annotation

  18. Semantic Annotation Metrics • There are no established metrics for semantic annotation: • No human-annotated corpora with precise class and instance information • No metrics for various partial matches • When a more specific class is recognized • When a more general class is recognized • When the class is correctly recognized, but the individual entity is not correctly identified.

  19. Conclusion • It is possible to adopt traditional IE techniques for semantic annotation • It is worth using almost-exhaustive entity knowledge for IE • KIM is still under development • Proper evaluation metrics • Precise disambiguation • More advanced IE techniques • KIM ontology and KB development

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