A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources

Dutta, Arnab ; Meilicke, Christian ; Ponzetto, Simone Paolo

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URL: https://madoc.bib.uni-mannheim.de/36461
Additional URL: https://madata.bib.uni-mannheim.de/65/
URN: urn:nbn:de:bsz:180-madoc-364610
Document Type: Conference or workshop publication
Year of publication: 2014
Book title: The Semantic Web: Semantics and Big Data : 11th International Conference, ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Proceedings
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 8465
Page range: 286-301
Date of the conference: 25 - 29 May 2014
Publisher: Presutti, Valentina
Place of publication: Berlin [u.a.]
Publishing house: Springer
ISBN: 978-3-319-07442-9
ISSN: 0302-9743 , 1611-3349
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Open Information Extraction (OIE) systems like Nell and ReVerb have achieved impressive results by harvesting massive amounts of machine-readable knowledge with minimal supervision. However, the knowledge bases they produce still lack a clean, explicit semantic data model. This, on the other hand, could be provided by full-fledged semantic networks like DBpedia or Yago, which, in turn, could benefit from the additional coverage provided by Web-scale IE. In this paper, we bring these two strains of research together, and present a method to align terms from Nell with instances in DBpedia. Our approach is unsupervised in nature and relies on two key components. First, we automatically acquire probabilistic type information for Nell terms given a set of matching hypotheses. Second, we view the mapping task as the statistical inference problem of finding the most likely coherent mapping – i.e., the maximum a posteriori (MAP) mapping – based on the outcome of the first component used as soft constraint. These two steps are highly intertwined: accordingly, we propose an approach that iteratively refines type acquisition based on the output of the mapping generator, and vice versa. Experimental results on gold-standard data indicate that our approach outperforms a strong baseline, and is able to produce ever-improving mappings consistently across iterations.
Additional information: http://dx.doi.org/10.1007/978-3-319-07443-6_20

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