%0 Book Section %1 Eiter07 %A Eiter, Thomas %D 2007 %J Logic Programming %K answer-set-programming, semantic-web %P 23--26 %R 10.1007/978-3-540-74610-2\_3 %T Answer Set Programming for the Semantic Web %U http://dx.doi.org/10.1007/978-3-540-74610-2\_3 %X The Semantic Web 1,2,3 aims at extending the current Web by standards and technologies that help machines to understand the information on the Web so that they can support richer discovery, data integration, navigation, and automation of tasks. Its development proceeds in layers, and the Ontology layer is the highest one that has currently reached a sufficient maturity, in the form of the OWL Web Ontology Language (OWL) 4,5, which is based on Description Logics. Current efforts are focused on realizing the Rules layer, which should complement the Ontology layer and offer sophisticated representation and reasoning capabilities. This raises, in particular, the issue of interlinking rules and ontologies. Excellent surveys that classify many proposals for combining rules and ontologies are 6,7; general issues that arise in this are discussed e.g. in 8,9,10. Notably, the World Wide Web Consortium (W3C) has installed The Rule Interchange Format (RIF) Working Group on order to produce a core rule language plus extensions which together allow rules to be translated between rule languages and thus transferred between rule systems; a first working draft has been released recently. Answer Set Programming (ASP) 11,12,13,14, also called A-Prolog 15,16,17, is a well-known declarative programming paradigm which has its roots in Logic Programming and Non-monotonic Reasoning 18. Thanks to its many extensions 19, ASP is well-suited for modeling and solving problems which involve common sense reasoning, and has been fruitfully applied to a range of applications including data integration, configuration, diagnosis, text mining, reasoning about actions and change, etc.; see 16,17,20. Within the context of the Semantic Web, the usage of ASP and related formalisms has been explored in different directions: •  On the one hand, they have been exploited as a tool to encode reasoning tasks in Description Logics, like 16,22,23,24,25,26,27. •  On the other hand, they have been used as a basis for giving a semantics to a combination of rules and ontologies. Here, increasing levels of integration have been considered: –  loose couplings, where rule and ontology predicates are separated, and the interaction is via a safe semantic interface like an inference relation e.g. 28,29,30,31,32 –  tight couplings, where rule and ontology predicates are separated, and the interaction is at the level of models, e.g. 33,34,35,36,37,38,39,10,40; and –  full integration, where no distinction between rule and ontology predicates is made, e.g., 41,42,43,44. In this tutorial, we will first briefly review ASP and ontology formalisms. We then will recall some of the issues that come up with the integration of rules and ontologies. After that, we will consider approaches to combine rules and ontologies under ASP, where particular attention well be devoted to non-monotonic description logic programs 45 and its derivatives 28,46 as a representative of loose couplings. However, also other approaches will be discussed. We further discuss the potential of such combinations, some applications, and finally some open issues.

@incollection{Eiter07, abstract = {{The Semantic Web [1,2,3] aims at extending the current Web by standards and technologies that help machines to understand the information on the Web so that they can support richer discovery, data integration, navigation, and automation of tasks. Its development proceeds in layers, and the Ontology layer is the highest one that has currently reached a sufficient maturity, in the form of the OWL Web Ontology Language (OWL) [4,5], which is based on Description Logics. Current efforts are focused on realizing the Rules layer, which should complement the Ontology layer and offer sophisticated representation and reasoning capabilities. This raises, in particular, the issue of interlinking rules and ontologies. Excellent surveys that classify many proposals for combining rules and ontologies are [6,7]; general issues that arise in this are discussed e.g. in [8,9,10]. Notably, the World Wide Web Consortium (W3C) has installed The Rule Interchange Format (RIF) Working Group on order to produce a core rule language plus extensions which together allow rules to be translated between rule languages and thus transferred between rule systems; a first working draft has been released recently. Answer Set Programming (ASP) [11,12,13,14], also called A-Prolog [15,16,17], is a well-known declarative programming paradigm which has its roots in Logic Programming and Non-monotonic Reasoning [18]. Thanks to its many extensions [19], ASP is well-suited for modeling and solving problems which involve common sense reasoning, and has been fruitfully applied to a range of applications including data integration, configuration, diagnosis, text mining, reasoning about actions and change, etc.; see [16,17,20]. Within the context of the Semantic Web, the usage of ASP and related formalisms has been explored in different directions: •\  On the one hand, they have been exploited as a tool to encode reasoning tasks in Description Logics, like [16,22,23,24,25,26,27]. •\  On the other hand, they have been used as a basis for giving a semantics to a combination of rules and ontologies. Here, increasing levels of integration have been considered: –\  loose couplings, where rule and ontology predicates are separated, and the interaction is via a safe semantic interface like an inference relation e.g. [28,29,30,31,32] –\  tight couplings, where rule and ontology predicates are separated, and the interaction is at the level of models, e.g. [33,34,35,36,37,38,39,10,40]; and –\  full integration, where no distinction between rule and ontology predicates is made, e.g., [41,42,43,44]. In this tutorial, we will first briefly review ASP and ontology formalisms. We then will recall some of the issues that come up with the integration of rules and ontologies. After that, we will consider approaches to combine rules and ontologies under ASP, where particular attention well be devoted to non-monotonic description logic programs [45] and its derivatives [28,46] as a representative of loose couplings. However, also other approaches will be discussed. We further discuss the potential of such combinations, some applications, and finally some open issues.}}, added-at = {2011-05-04T16:04:17.000+0200}, author = {Eiter, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/224f0828bbcd5a0d8068603ab86ba6b86/baisemain}, citeulike-article-id = {5274013}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-74610-2\_3}, citeulike-linkout-1 = {http://www.springerlink.com/content/ep1014l8p733q260}, doi = {10.1007/978-3-540-74610-2\_3}, interhash = {4aae7edf95e7a293689580ecb8f16631}, intrahash = {24f0828bbcd5a0d8068603ab86ba6b86}, journal = {Logic Programming}, keywords = {answer-set-programming, semantic-web}, pages = {23--26}, posted-at = {2009-07-26 19:43:46}, priority = {2}, timestamp = {2011-05-04T16:04:33.000+0200}, title = {{Answer Set Programming for the Semantic Web}}, url = {http://dx.doi.org/10.1007/978-3-540-74610-2\_3}, year = 2007 }