Integration of Large Scale Knowledge Bases using Probabilistic Graphical Models

Dutta, Arnab

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URL:	https://ub-madoc.bib.uni-mannheim.de/35518
Additional URL:	https://madata.bib.uni-mannheim.de/65/
URN:	urn:nbn:de:bsz:180-madoc-355189
Document Type:	Conference or workshop publication
Year of publication:	2014
Book title:	WSDM'14 : proceedings of the 7th ACM International Conference on Web Search and Data Mining; February 24 - 28, 2014, New York, NY, USA
Page range:	643-648
Date of the conference:	24-28 February 2014
Place of publication:	New York, NY
Publishing house:	ACM
ISBN:	978-1-4503-2351-2
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject:	004 Computer science, internet
Abstract:	Over the recent past, information extraction (IE) systems such as Nell and ReVerb have attained much success in creating large knowledge resources with minimal supervision. But, these resources in general, lack schema information and contain facts with high degree of ambiguity which are often difficult to interpret. Whereas, Wikipedia-based IE projects like DBpedia and Yago are structured, have disambiguated facts with unique identifiers and maintain a well-defined schema. In this work, we propose a probabilistic method to integrate these two types of IE projects where the structured knowledge bases benefit from the wide coverage of the semi-supervised IE projects and the latter benefits from the schema information of the former.
Additional information:	http://dx.doi.org/10.1145/2556195.2556202