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Transactions on Computational Collective Intelligence II pp 1–23Cite as

Integration Proposal for Description Logic and Attributive Logic – Towards Semantic Web Rules

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Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 6450))

Abstract

The current challenge of the Semantic Web is the development of an expressive yet effective rule language. This paper presents an integration proposal for Description Logics (DL) and Attributive Logics (ALSV) is presented. These two formalisms stem from fields of Knowledge Representation and Artificial Intelligence. However, they are based on different design goals and therefore provide different description and reasoning capabilities. ALSV is the foundation of XTT2, an expressive language for rule-based systems. DL provide formulation for expressive ontology languages such as OWL2. An important research direction is the development of rule languages that can be integrated with ontologies. The contribution of the paper consists in introducing a possible transition from ALSV to DL. This opens up possibilities of using XTT2, a well-founded rule-based system modelling rule language, to improve the design of Semantic Web rules.

The paper is supported by the BIMLOQ Project funded from 2010–2012 resources for science as a research project.

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Authors and Affiliations

  1. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Grzegorz J. Nalepa & Weronika T. Furmańska

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  1. Grzegorz J. Nalepa
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  2. Weronika T. Furmańska
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  1. Institute of Informatics, Wroclaw University of Technology, Str. Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Centre for Complex Software Systems and Services, Swinburne University of Technology, P.O. Box 218, 3122, Hawthorn, Victoria, Australia

    Ryszard Kowalczyk

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Nalepa, G.J., Furmańska, W.T. (2010). Integration Proposal for Description Logic and Attributive Logic – Towards Semantic Web Rules. In: Nguyen, N.T., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence II. Lecture Notes in Computer Science, vol 6450. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17155-0_1

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  • DOI: https://doi.org/10.1007/978-3-642-17155-0_1

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