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International Workshop on Uncertainty Reasoning for the Semantic Web

International Workshop on Uncertainty Reasoning for the Semantic Web

International Workshop on Uncertainty Reasoning for the Semantic Web

URSW 2006, URSW 2007, URSW 2005: Uncertainty Reasoning for the Semantic Web I pp 282–314Cite as

Towards Machine Learning on the Semantic Web

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5327))

Abstract

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.

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Authors and Affiliations

  1. Siemens AG, Corporate Technology, Information and Communications, Learning Systems, Munich, Germany

    Volker Tresp & Yi Huang

  2. Ludwig-Maximilian University Munich, Germany

    Markus Bundschus

  3. Technical University of Munich, Germany

    Achim Rettinger

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  1. Volker Tresp
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  2. Markus Bundschus
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  3. Achim Rettinger
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  4. Yi Huang
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Editors and Affiliations

  1. George Mason University, 4400 University Drive, Fairfax, 22030, Virginia, USA

    Paulo Cesar G. da Costa

  2. Dipartimento di Informatica,, Università degli Studi di Bari, Via Orabona, 4, 70125, Bari, Italy

    Claudia d’Amato

  3. Dipartimento di Informatica, Università degli Studi di Bari, Via Orabona, 4, 70125, Bari, Italy

    Nicola Fanizzi

  4. George Mason University, VA 22030, Fairfax, USA

    Kathryn B. Laskey

  5. MIRTE Corporation, M/S H305, 7515 Colshire Drive, VA 22102-7508, McLean, USA

    Kenneth J. Laskey

  6. Computing Laboratory, Oxford University, Wolfson Building, Parks Road, Oxford OX1 3QD, UK

    Thomas Lukasiewicz

  7. University of Bath, BA2 7AY, Bath, UK

    Matthias Nickles

  8. Convera, Inc., 1921 Gallows Road, Suite 200, VA 22182, Vienna, USA

    Michael Pool

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Tresp, V., Bundschus, M., Rettinger, A., Huang, Y. (2008). Towards Machine Learning on the Semantic Web. In: da Costa, P.C.G., et al. Uncertainty Reasoning for the Semantic Web I. URSW URSW URSW 2006 2007 2005. Lecture Notes in Computer Science(), vol 5327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89765-1_17

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