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Materializing and Querying Learned Knowledge

Volker Tresp, Markus Bundschus, Yi Huang, Achim Rettinger

Published: 2009

Buchtitel: In Proceedings of the First ESWC Workshop on Inductive Reasoning and Machine Learning on the Semantic Web (IRMLeS 2009)
Ausgabe: 474
Verlag: RWTH Aachen
Referierte Veröffentlichung

In many Semantic Web domains a tremendous number of statements (expressed as triples) can potentially be true but, in a given domain, only a small number of statements is known to be true or can be inferred to be true. It thus makes sense to attempt to estimate the truth values of statements by exploring regularities in the Semantic Web data via machine learning. Our goal is a “push-button” learning approach that requires a minimum of user intervention. The learned knowledge is materialized off-line (at loading time) such that querying is fast. We define an extension of SPARQL for the integration of the learned probabilistic statements into querying. The proposed approach deals well with typical properties of Semantic Web data. i.e., with the sparsity of the data and with missing data. Statements that can be inferred via logical reasoning can readily be integrated into learning and querying. We study learning algorithms that are suitable for the resulting high-dimensional sparse data matrix. We present experimental results using a friend-of-a-friend data set.

ISSN: 1613-0073
Download: Media:Tresp irmles09 MaQLK.pdf
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