Enriching structured knowledge with open information

Dutta, Arnab ; Meilicke, Christian ; Stuckenschmidt, Heiner

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DOI: https://doi.org/10.1145/2736277.2741139
URL: https://madoc.bib.uni-mannheim.de/38861
Additional URL: http://dl.acm.org/citation.cfm?id=2741139
URN: urn:nbn:de:bsz:180-madoc-388619
Document Type: Conference or workshop publication
Year of publication: 2015
Book title: Proceedings of the 24th International Conference on World Wide Web, {WWW} 2015, Florence, Italy, May 18-22, 2015
Page range: 267-277
Date of the conference: 18-22 May 2015
Publisher: Gangemi, Aldo
Place of publication: New York, NY
Publishing house: ACM
ISBN: 978-1-4503-3469-3
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): Markov clustering , data integration , enriching knowledge bases , probabilistic inference
Abstract: We propose an approach for semantifying web extracted facts. In particular, we map subject and object terms of these facts to instances; and relational phrases to object properties defined in a target knowledge base. By doing this we resolve the ambiguity inherent in the web extracted facts, while simultaneously enriching the target knowledge base with a significant number of new assertions. In this paper, we focus on the mapping of the relational phrases in the context of the overall work ow. Furthermore, in an open extraction setting identical semantic relationships can be represented by different surface forms, making it necessary to group these surface forms together. To solve this problem we propose the use of markov clustering. In this work we present a complete, ontology independent, generalized workflow which we evaluate on facts extracted by Nell and Reverb. Our target knowledge base is DBpedia. Our evaluation shows promising results in terms of producing highly precise facts. Moreover, the results indicate that the clustering of relational phrases pays of in terms of an improved instance and property mapping.

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