{"type":"ScholarlyArticle","title":"A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations","authors":[{"name":"Kangqi Luo","slug":"kangqi-luo","given":"Kangqi","family":"Luo","affiliation":"Shanghai Jiao Tong University , Shanghai , China"},{"name":"Xusheng Luo","slug":"xusheng-luo","given":"Xusheng","family":"Luo","affiliation":"Shanghai Jiao Tong University , Shanghai , China"},{"name":"Xianyang Chen","slug":"xianyang-chen","given":"Xianyang","family":"Chen","affiliation":"Shanghai Jiao Tong University , Shanghai , China"},{"name":"Kenny Q. Zhu","slug":"kenny-q-zhu","given":"Kenny Q.","family":"Zhu","affiliation":"Shanghai Jiao Tong University , Shanghai , China"}],"abstract":"This paper studies the problem of discovering the structured knowledge representation of binary natural language relations.The representation, known as the schema, generalizes the traditional path of predicates to support more complex semantics.We present a search algorithm to generate schemas over a knowledge base, and propose a data-driven learning approach to discover the most suitable representations to one relation. Evaluation results show that inferred schemas are able to represent precise semantics, and can be used to enrich manually crafted knowledge bases.","doi":"10.24963/ijcai.2017/163","url":"https://ijcai.org/proceedings/2017/163","pdf":"https://ijcai.org/proceedings/2017/0163.pdf","markdown":"https://ijcai.org/proceedings/2017/163.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/163","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":163,"pages":{"first":1174,"last":1180},"keywords":["Knowledge Representation, Reasoning, and Logic: Logics for Knowledge Representation","Knowledge Representation, Reasoning, and Logic: Reasoning about Knowlege and Belief","Machine Learning: Knowledge-based Learning"],"videos":[],"references":[{"position":1,"raw":"[Auer et al., 2007] Sören Auer, Christian Bizer, Georgi Ko- bilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 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