Combining semantic web search with the power of inductive reasoning

  • Authors:

  • Claudia d'Amato;Nicola Fanizzi;Bettina Fazzinga;Georg Gottlob;Thomas Lukasiewicz

  • Affiliations:

  • Dipartimento di Informatica, Università degli Studi di Bari, Italy;Dipartimento di Informatica, Università degli Studi di Bari, Italy;Dipartimento di Elettronica, Informatica e Sistemistica, Università della Calabria, Italy;Computing Laboratory, University of Oxford, UK and Oxford-Man Institute of Quantitative Finance, University of Oxford, UK;Computing Laboratory, University of Oxford, UK and Institut für Informationssysteme, TU Wien, Austria

  • Venue:
  • SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
  • Year:
  • 2010

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Abstract

With the introduction of the SemanticWeb as a future substitute of the Web, the key task for the Web, namely, Web Search, is evolving towards some novel form of Semantic Web search. A very promising recent approach to SemanticWeb search is based on combining standardWeb pages and search queries with ontological background knowledge, and using standard Web search engines as the main inference motor of Semantic Web search. In this paper, we continue this line of research. We propose to further enhance this approach by the use of inductive reasoning. This increases the robustness of Semantic Web search, as it adds the important ability to handle inconsistencies, noise, and incompleteness, which are all very likely to occur in distributed and heterogeneous environments such as the Web. In particular, inductive reasoning allows to infer (from training individuals) new knowledge, which is not logically deducible. We also report on a prototype implementation of the new approach and its experimental evaluations.