Welcome to the InfoSci Platform
Scalable Reasoning with Tractable Fuzzy Ontology Languages

Scalable Reasoning with Tractable Fuzzy Ontology Languages

Giorgos Stoilos, Jeff Z. Pan, Giorgos Stamou
Copyright: © 2010 |Pages: 29
ISBN13: 9781605668581|ISBN10: 1605668583|ISBN13 Softcover: 9781616924478|EISBN13: 9781605668598
DOI: 10.4018/978-1-60566-858-1.ch005
Cite Chapter Cite Chapter

MLA

Stoilos, Giorgos, et al. "Scalable Reasoning with Tractable Fuzzy Ontology Languages." Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, IGI Global, 2010, pp. 130-158. https://doi.org/10.4018/978-1-60566-858-1.ch005

APA

Stoilos, G., Pan, J. Z., & Stamou, G. (2010). Scalable Reasoning with Tractable Fuzzy Ontology Languages. In A. Laurent & M. Lesot (Eds.), Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design (pp. 130-158). IGI Global. https://doi.org/10.4018/978-1-60566-858-1.ch005

Chicago

Stoilos, Giorgos, Jeff Z. Pan, and Giorgos Stamou. "Scalable Reasoning with Tractable Fuzzy Ontology Languages." In Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, 130-158. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-858-1.ch005

Export Reference

Mendeley
Favorite

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.