KWTR: Fuzzy reasoning

== Contributors: == Giorgos Stoilos - National and Technical University of Athens please write your name and affiliation

Please add your CV in the list of contributors

• What is the state of the art of Semantic Web in your research field?

During the last years much work has been done in the topic of extending and studying the semantics of Semantic Web ontology languages, like Descriptions Logics and OWL and rule languages, like SWRL and RuleML with fuzzy sets and fuzzy set theoretic operators. More precisely, fuzzy SHOIN(D) was presented by Straccia [1], while syntax (abstract and XML/RDF), semantics and reduction to fuzzy DLs of Fuzzy OWL has been presented by Stoilos et. al. [2]. On the other hand Fuzzy SWRL was presented by Pan et. al. [3] and an approach to represent several uncertainty logic programming approaches with Fuzzy RuleML by Damasio et. al. [4].

Regarding reasoning with fuzzy ontologies, since the first significant work by Straccia [5] on the basic DL fuzzy-ALC there has been a significant advancement in reasoning algorithms and methods for fuzzy DLs, by Stoilos et. al. on fuzzy-SI [6] fuzzy-SHIN [7], fuzzy-SHOIN [8] and GCIs in fuzzy DL by Stoilos et. al. [9]. It is also worth mentioning the work of Straccia in reasoning with fuzzy concrete domains fuzzy-ALC(D) [10]

• Provide references and short abstracts of three papers you consider as significant in your research field.

Straccia, Umberto. Towards a Fuzzy Description Logic for the Semantic Web (Preliminary Report). In Proceedings of the 2nd European Semantic Web Conference (ESWC-05), 2005.

Abstract: In this paper we present a fuzzy version of SHOIN(D), the corresponding Description Logic of the ontology description language OWL DL. We show that the representation and reasoning capabilities of fuzzy SHOIN(D) go clearly beyond classical SHOIN(D). We present its syntax and semantics. Interesting features are that concrete domains are fuzzy and entailment and subsumption relationships may hold to some degree in the unit interval [0,1].

G. Stoilos, N. Simou, G. Stamou and S. Kollias. Uncertainty and the Semantic Web IEEE Intelligent Systems, 21(5), p. 84-87, 2006.

Abstract: The Semantic Web must handle applications that face uncertain and imprecise information, including random, fuzzy, missing, and distorted knowledge. Many researchers have proposed extending OWL and Description Logic to deal with such uncertainty. f-OWL, a fuzzy extension to OWL, can capture imprecise and vague knowledge. The accompanying Fuzzy Reasoning Engine lets f-OWL capture and reason about such knowledge.

Damasio, Carlos Viegas and Pan, Jeff Z. and Stoilos, Giorgos and Straccia, Umberto. An Approach to Representing Uncertainty Rules in RuleML. In Proceeedings of the 2nd International Conference on Rules and Rule Markup Languages for the Semantic Web (RuleML-06), 2006.

Abstract: The RuleML initiative defines a normalized markup for expressing and exchange rules in the Semantic Web. However, the syntax of the language is still limited and lacks features for representing rule-based languages capable of handling uncertainty. It is desirable to have a general extension of RuleML which accommodates major existing languages proposed in the latest two decades. The main contribution of the paper is to propose such a general extension, showing how to encode many of the existing languages in this extension. We hope this work can also provide some insights on how to cover uncertainty in the RIF framework.

• Please provide one or more examples (either business, or research, or both) in which semantic web has been used (if you can, add some references).

Medical applications for building a medical vocabulary using the Semantic Web language OWL [11].

• Are there existing tools or demos? Please indicate some of them.

The FiRE Fuzzy Reasoning Engine is a tableaux based fuzzy DL reasoning for fuzzy SHIN. It has been implemented in Java and it is available at: [12]. FiRE is aimed to be a complete integrated platform and besides reasoning with fuzzy-SHIN it also provides functionalities for serializing fuzziness into RDF triples and storing them into Sesame as well as expressive fuzzy querying over the stored knowledge with a form of extended SeRQL queries.

The fuzzyDL [13] is a Description Logic Reasoner supporting Fuzzy Logic reasoning. The fuzzyDL system includes a reasoner for fuzzy SHIf with concrete fuzzy concepts (ALC augmented with transitive roles, a role hierarchy, inverse roles, functional roles, and explicit definition of fuzzy sets).

• What are the open problems in your Semantic Web research field? Why?

Building reasoners, management tools (editors, storing systems, etc) and ontologies. Besides the purely theoretical work on semantics and reasoning algorithms there is great need for providing tool support for editing, managing and storing fuzzy ontologies.

Reasoning with other norm operators in fuzzy DLs: Most algorithms for fuzzy DLs use specific fuzzy operators for the fuzzy language and more precisely the standard operators: min(a,b) for intersection, max(a,b) for union, 1-a for negation and max(1-a,b) for implication. The extension to other operators is important since they provide different properties and are used in different settings.

Extending tractable fragments of Description Logics with fuzzinees. Tractability is an important issue in general, hence providing fuzzy extensions to tractable fragments is an important issue for providing scalable fuzziness handling formalisms.

Integrations with fuzzy rules. Similar to the classical problem of integrating Description Logics and rule languages.

• Provide references and links of the most relevant Semantic Web research projects in your field.

Knowledge Web [14] - Realizing the Semantic Web, Semantic-Based Knowledge Systems in the Language Extensions Work Package has studied fuzzy extensions to ontology languages.

X-Media [15] - Large Scale Knowledge Sharing and Reuse Across Media. Also has a WP especially focused on Uncertain Knowledge management and reasoning.

K-Space [16] - Knowledge Space of Semantic Inference for Automatic Annotation and Retrieval of Multimedia Content. The project pays special care in the management of uncertain information in Multimedia documents with the aid of fuzziness handling Semantic Web languages like fuzzy-OWL.

• What challenges try these projects to overcome?

Knowledge web is a Network of Excellence for advancing the research in the Semantic Web

x-Media is an Integrated Project for providing large-scale scalable and distributed methodologies and tools for multimedia systems.

K-Space is a Network of Excellence aiming at narrowing the gap between content descriptors that can be computed automatically by current machines and algorithms, and the richness and subjectivity of semantics in high-level human interpretations of audiovisual media

• What are their foreseen benefits (both in market and scientific community)?

Tools and methodologies for adding multimedia applications in the Semantic Web as well as new methods for multimedia document analysis and interpretation.

• When, in your opinion, will projects’ results be ready for industry?

In most aforementioned projects there are several industrial partners involved, hence the adoption will in general be fast.

• Do you think that it is important to invest (money and time) in these topics? Why?

Yes because a big portion of Semantic Web applications or applications that adapt Semantic Web technologies in order to publish content on the Web require the management of uncertainty and fuzziness. The adoption of Semantic Web technologies, like languages in these applications is very slow and in general will not be successful unless uncertainty and fuzziness handling requirements are not fulfilled.

• What are, in your opinion, the most relevant Semantic Web challenges that will be solved in the long term (10 years)? Why?

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