Scalable Reasoning with Tractable Fuzzy Ontology Languages

Scalable Reasoning with Tractable Fuzzy Ontology Languages

Giorgos Stoilos (National and Technical University of Athens, Greece), Jeff Z. Pan (University of Aberdeen, UK) and Giorgos Stamou (National and Technical University of Athens, Greece)
DOI: 10.4018/978-1-60566-858-1.ch005
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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.
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Nowadays, many applications and domains use some form of knowledge representation language and exploit their inference mechanisms in order to improve their capabilities and simulate intelligent human behavior. Many such examples exist, like knowledge-based multimedia analysis (Neumann & Möller, 2006; Simou et al., 2008a), bioinformatics (Dameron et al., 2004) and databases (Calvanese et al., 1998) and more. Nevertheless the most prominent example is undoubtedly the World Wide Web aiming for intelligently managing the vast amount of information that lays on the Web. Among several proposals for structuring knowledge in such applications, Description Logic based ontologies seem to be an approach that has gained considerable attention. Description Logics (DLs) (Baader et al., 2002) is a modern knowledge representation formalism that is a fragment of First-Order Logic, enjoying well-defined model-theoretic semantics, decidability and practically efficient reasoning systems. Most importantly expressive DLs form the logical underpinnings of the W3C standard language for representing ontologies in the Semantic Web, namely OWL (Bechhofer et al., 2004; Patel-Schneider et al., 2004). Although several successful OWL DL reasoning systems have been developed, like FaCT++1 and Pellet2, even very basic and inexpressive DLs come with come with (at least) 978-1-60566-858-1.ch005.m01 computational complexity. Thus, their ability to scale in large application like the once found on the Web is still an open issue. For those reasons the last years great research effort has been focusing in identifying fragments/clusters of the OWL DL language for which it is known that reasoning is scalable and efficient. This research has led to the development of several languages, but the two most interesting and predominant ones are EL+ (Baader et al.) and DL-Lite (Calvanese et al., 2005; Calavanese et al., 2007). The interesting thing is that these languages will most likely form the logical underpinnings of the OWL 2 EL and OWL 2 QL3 recommendations which consist of profiles/fragments of the upcoming extension of OWL, OWL 24.

Although DLs are relatively quite expressive they feature limitations mainly with what can be said about imperfect (uncertain, vague/fuzzy or imprecise) knowledge. Such types of knowledge appears in many domains but also in several Semantic Web tasks, like in the representation of trust, in knowledge fusion, assessing the similarity between resources and many more. For those reasons fuzzy ontologies are envisioned to be useful in the Web (Stoilos et al., 2006) and fuzzy Description Logics (f-DLs) (Höldobler et al., 2005; Straccia, 2001; Tresp & Molito, 1998) have been proposed as formalisms capable of capturing and reasoning with such knowledge. Research in f-DLs was mainly focused on providing reasoning support for very expressive fuzzy DLs, like reasoning with the f-DL fKD-SHIN (Stoilos et al., 2007; Stoilos et al. 2005b), reasoning with fKD-SHI (Li et al., 2006), supporting reasoning in f-DLs that allow for general concept inclusion axioms (Li et al., 2006; Stoilos et al., 2006), fuzzy extensions of the OWL language (Stoilos et al., 2005a) supporting expressive datatypes (Wang et al., 2008) or adding more expressive fuzzy features, like comparison expressions (Kang et al., 2006; Lu et al., 2008) and concept modifiers (Hölldobler et al., 2006; Wang et al., 2006). Interestingly, there also exist two f-DL reasoners, FiRE5 (Stoilos et al., 2007), which supports fKD-SHIN and the fuzzyDL6 (Straccia, 2008), which supports fKD-SHIf(D) and fL-SHIf(D). Unfortunately, like their crisp counterparts, fuzzy-SHIN and fuzzy-SHIf(D) come with (at least) 978-1-60566-858-1.ch005.m02 computational complexity. Additionally, the practical behavior of implementations of such logics would also have to deal with the degrees thus adding more to the practical complexity.

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