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220/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao
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Uploaded byRadityo Eko Prasojo
Entity-Relationship Extraction from Wikipedia Unstructured Text - Overview
The document discusses the challenges and methodologies associated with extracting entity-relationship data from unstructured Wikipedia text. It highlights difficulties such as entity extraction, relation extraction, and the representation of complex information without a predefined schema. The ongoing work includes automated rule mining, semantic refinement for knowledge representation, and future endeavors in metadata extraction and natural language question answering.
Entity-Relationship Extraction from Wikipedia Unstructured Text - Overview
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- 9. Why is it difficult? •The extraction problem • Entity extraction & disambiguation • Relation extraction •The representation problem •Lack of predefined schema/ontology • Topic-independency • Complex fact representation 20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 9
- 10. Why is it difficult? Example • “Obama is a supporter of the Chicago White Sox” •Straightforward, singleton information • Pure syntactic extraction possible • Barack_Obama supporterOf Chicago_White_Sox 20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 10
- 11. Why is it difficult? Example • “Obama is a supporter of the Chicago White Sox” •Straightforward, singleton information • Pure syntactic extraction possible • Barack_Obama supporterOf Chicago_White_Sox • “He is also primarily a Chicago Bears football fan in the NFL, but in his childhood and adolescence was a fan of the Pittsburgh Steelers” • Complex, multiple information • Semantic understanding necessary • … how do we represent this? 20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 11
- 12. Example: representing complex fact • “He is also primarilya Chicago Bears football fan in the NFL, but in his childhood and adolescence was a fan of the Pittsburgh Steelers” • Barack_Obama footballFan Chicago_Bears in NFL • supporterOf vs footballFan • Is it necessary to include NFL in the whole relations? • What about the adjective primarily? What information does it imply? • Barack_Obama fanOf Pittsburgh_Steelers • fanOf vs supporterOf • Missing the time information referred in “in his childhood and adolescence was” 20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 12
- 13. Approach • Document preprocessing to annotate all entity occurences. • Grammatical dependency to extract (candidate) relations. •Separation between the extraction problem and the representation problem • We first extract all candidate relations and then later apply semantic refinement for better representation. 20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 13
- 14. Preliminary results • Ground truth manually curated from 25 Wikipedia articles of famous people. • Preprocessing •4 handcrafted extraction rules leveraging grammatical dependency 20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 14
- 15. Ongoing work • Automated rule mining • Semantic refinement for knowledge representation •Ontology building • Naming and taxonomy of entities, classes, and relations • Handling complex fact • Obama appoints x as y in z • Handling modality, adjectives, and sentiment • “In the past”, “it is rumoured that”, “it is not true that” • Future evaluation • Bigger ground truth (amount + topic coverage) • Evaluate how well we enrich existing KBs 20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 15
- 16. Future work • Metadata extraction • Data quality, data completeness •Natural language question answering based on the enriched KB. 20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 16