A workbench for anytime reasoning by ontology approximation: With a case study on instance retrieval

Gaston Tagni*, Stefan Schlobach, Annette Ten Teije, Frank Van Harmelen, Giorgios Karafotias

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Reasoning is computationally expensive. This is especially true for reasoning on the Web, where data sets are very large and often described by complex terminologies. One way to reduce this complexity is through the use of approximate reasoning methods which trade one computational property (eg. quality of answers) for others, such as time and memory. Previous research into approximation on the Semantic Web has been rather ad-hoc, and we propose a framework for systematically studying such methods. We developed a workbench which allows the structured combination of different algorithms for approximation, reasoning and measuring in one single framework. As a case-study we investigate an incremental method for instance retrieval through ontology approximation, and we use our workbench to study the computational behaviour of several approximation strategies.

Original languageEnglish
Title of host publicationSTAIRS 2010 Proceedings of the Fifth Starting AI Researchers' Symposium
PublisherIOS Press
Pages328-340
Number of pages13
Volume222
ISBN (Print)9781607506751
DOIs
Publication statusPublished - 2010

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume222
ISSN (Print)09226389

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Keywords

  • Anytime Reasoning
  • Approximate Reasoning
  • Description Logics
  • Ontologies
  • Semantic Web

Cite this

Tagni, G., Schlobach, S., Ten Teije, A., Van Harmelen, F., & Karafotias, G. (2010). A workbench for anytime reasoning by ontology approximation: With a case study on instance retrieval. In STAIRS 2010 Proceedings of the Fifth Starting AI Researchers' Symposium (Vol. 222, pp. 328-340). (Frontiers in Artificial Intelligence and Applications; Vol. 222). IOS Press. https://doi.org/10.3233/978-1-60750-676-8-328