Type Inference on Noisy RDF Data


Paulheim, Heiko ; Bizer, Christian



DOI: https://doi.org/10.1007/978-3-642-41335-3_32
URL: http://www.heikopaulheim.com/docs/iswc2013.pdf
Additional URL: http://de.slideshare.net/heikopaulheim/type-infere...
Document Type: Conference or workshop publication
Year of publication: 2013
Book title: The Semantic Web - ISWC 2013 : 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21-25, 2013, Proceedings, Part I
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 8218
Page range: 510-525
Date of the conference: 21-25 October 2013
Publisher: Alani, Harith
Place of publication: Berlin [u.a.]
Publishing house: Springer
ISBN: 978-3-642-41334-6 , 978-3-642-41335-3
Publication language: English
Institution: School of Business Informatics and Mathematics > Wirtschaftsinformatik V (Bizer)
Subject: 004 Computer science, internet
Keywords (English): Type Inference , Noisy Data , Link-based Classification , RDF , Semantic Web
Abstract: Type information is very valuable in knowledge bases. However, most large open knowledge bases are incomplete with respect to type information, and, at the same time, contain noisy and incorrect data. That makes classic type inference by reasoning difficult. In this paper, we propose the heuristic link-based type inference mechanism SD- Type, which can handle noisy and incorrect data. Instead of leveraging T-box information from the schema, SDType takes the actual use of a schema into account and thus is also robust to misused schema elements.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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