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Towards Machine Learning on the Semantic Web

Volker Tresp, Markus Bundschus, Achim Rettinger, Yi Huang

Veröffentlicht: 2008
Herausgeber: Paulo Cesar Costa, Claudia D'Amato, Nicola Fanizzi, Kathryn B. Laskey, Kenneth J. Laskey, Thomas Lukasiewicz, Matthias Nickles, Michael Pool
Buchtitel: Uncertainty Reasoning for the Semantic Web I
Seiten: 282-314
Verlag: Springer


In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

ISBN: 978-3-540-89764-4
Download: Media:Tresp urswI TMLotSW.pdf
DOI Link: 10.1007/978-3-540-89765-1_17