Scalable reasoning with tractable fuzzy ontology languages

Giorgos Stoilos, Jeff Z. Pan, Giorgos Stamou

Research output: Chapter in Book/Report/Conference proceedingChapter


The last couple of years it is widely acknowledged that uncertainty and fuzzy extensions to ontology languages, like description logics (DLs) and OWL, could play a significant role in the improvement of many Semantic Web (SW) applications like matching, merging and ranking. Unfortunately, existing fuzzy reasoners focus on very expressive fuzzy ontology languages, like OWL, and are thus not able to handle the scale of data that the Web provides. For those reasons much research effort has been focused on providing fuzzy extensions and algorithms for tractable ontology languages. In this chapter, the authors present some recent results about reasoning and fuzzy query answering over tractable/polynomial fuzzy ontology languages namely Fuzzy DL-Lite and Fuzzy EL+. Fuzzy DL-Lite provides scalable algorithms for very expressive (extended) conjunctive queries, while Fuzzy EL+ provides polynomial algorithms for knowledge classification. For the Fuzzy DL-Lite case the authors will also report on an implementation in the ONTOSEARCH2 system and preliminary, but encouraging, benchmarking results.

Original languageEnglish
Title of host publicationScalable Fuzzy Algorithms for Data Management and Analysis
Subtitle of host publicationMethods and Design
EditorsAnne Laurent, Marie-Jeanne Lesot
Publisher IGI Global
Number of pages29
ISBN (Electronic)9781605668598
ISBN (Print)9781605668581, 1605668583, 9781616924478
Publication statusPublished - 1 Dec 2009


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