Type prediction in RDF knowledge bases using hierarchical multilabel classification

Melo, André ; Paulheim, Heiko ; Völker, Johanna

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DOI: https://doi.org/10.1145/2912845.2912861
URL: https://madoc.bib.uni-mannheim.de/40970
Additional URL: http://dl.acm.org/citation.cfm?doid=2912845.291286...
URN: urn:nbn:de:bsz:180-madoc-409704
Document Type: Conference or workshop publication
Year of publication: 2016
Book title: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016, Nîmes, France, June 13-15, 2016
Page range: Article 14, 1-10
Conference title: 6th International Conference on Web Intelligence, Mining and Semantics, {WIMS} 2016
Location of the conference venue: Nimes, France
Date of the conference: June 13-15, 2016
Publisher: Akerkar, Rajendra
Place of publication: New York, NY
Publishing house: ACM
ISSN: 978-1-4503-4056-4
Publication language: English
Institution: School of Business Informatics and Mathematics > Information Systems V: Web-based Systems (Bizer 2012-)
School of Business Informatics and Mathematics > Web Data Mining (Juniorprofessur) (Paulheim 2013-2017)
Subject: 004 Computer science, internet
Abstract: Large Semantic Web knowledge bases are often noisy, incorrect, and incomplete with respect to type information. Automatic type prediction can help reduce such incompleteness, and, as previous works show, statistical methods are well-suited for this kind of data. Since most Semantic Web knowledge bases come with an ontology defining a type hierarchy, in this paper, we rephrase the type prediction problem as a hierarchical multilabel classification problem. We propose SLCN, a modification of the local classifier per node approach, which performs feature selection, instance sampling, and class balancing for each local classifier. Our approach improves scalability, facilitating its application on large Semantic Web datasets with high-dimensional feature and label spaces. We compare the performance of our proposed method with a state-of-the-art type prediction approach and popular hierarchical multilabel classifiers, and report on experiments with large-scale RDF datasets.

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BASE: Melo, André ; Paulheim, Heiko ; Völker, Johanna

Google Scholar: Melo, André ; Paulheim, Heiko ; Völker, Johanna

ORCID: Melo, André ; Paulheim, Heiko ORCID: 0000-0003-4386-8195 ; Völker, Johanna

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