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
Weitere URL:	https://madata.bib.uni-mannheim.de/65/
URN:	urn:nbn:de:bsz:180-madoc-355189
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2014
Buchtitel:	WSDM'14 : proceedings of the 7th ACM International Conference on Web Search and Data Mining; February 24 - 28, 2014, New York, NY, USA
Seitenbereich:	643-648
Veranstaltungsdatum:	24-28 February 2014
Ort der Veröffentlichung:	New York, NY
Verlag:	ACM
ISBN:	978-1-4503-2351-2
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Fachgebiet:	004 Informatik
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.
Zusätzliche Informationen:	http://dx.doi.org/10.1145/2556195.2556202