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WORL: A Web ontology rule language
- In Proceedings of KSE’2011
, 2011
"... Abstract. We develop a new Web ontology rule language, called WORL, which combines a variant of OWL 2 RL with eDatalog ¬. We allow additional features like negation, the minimal number restriction and unary external checkable predicates to occur in the left hand side of concept inclusion axioms. Som ..."
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Abstract. We develop a new Web ontology rule language, called WORL, which combines a variant of OWL 2 RL with eDatalog ¬. We allow additional features like negation, the minimal number restriction and unary external checkable predicates to occur in the left hand side of concept inclusion axioms. Some restrictions are adopted to guarantee a translation into eDatalog ¬. We also develop the well-founded semantics for WORL and the standard semantics for stratified WORL (SWORL) via translation into eDatalog ¬. Both WORL and SWORL have PTime data complexity. In contrast to the existing combined formalisms, in WORL and SWORL negation in concept inclusion axioms is interpreted using nonmonotonic semantics.
A Bisimulation-based Method of Concept Learning for Knowledge Bases in Description Logics Quang-Thuy Ha ∗ , Thi-Lan-Giao Hoang † , Linh Anh Nguyen ‡,
"... Abstract — We develop the first bisimulation-based method of concept learning, called BBCL, for knowledge bases in description logics (DLs). Our method is formulated for a large class of useful DLs, with well-known DLs like ALC, SHIQ, SHOIQ, SROIQ. As bisimulation is the notion for characterizing in ..."
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Abstract — We develop the first bisimulation-based method of concept learning, called BBCL, for knowledge bases in description logics (DLs). Our method is formulated for a large class of useful DLs, with well-known DLs like ALC, SHIQ, SHOIQ, SROIQ. As bisimulation is the notion for characterizing indiscernibility of objects in DLs, our method is natural and very promising. I.
Linh Anh Nguyen §, Hung Son Nguyen §,
"... Abstract—The work [1] by Nguyen and Szałas is a pioneering one that uses bisimulation for machine learning in the context of description logics. In this paper we generalize and extend their concept learning method [1] for description logic-based information systems. We take attributes as basic eleme ..."
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Abstract—The work [1] by Nguyen and Szałas is a pioneering one that uses bisimulation for machine learning in the context of description logics. In this paper we generalize and extend their concept learning method [1] for description logic-based information systems. We take attributes as basic elements of the language. Each attribute may be discrete or numeric. A Boolean attribute is treated as a concept name. This approach is more general and much more suitable for practical information systems based on description logic than the one of [1]. As further extensions we allow also data roles and the concept constructors “functionality” and “unquantified number restrictions”. We formulate and prove an important theorem on basic selectors. We also provide new examples to illustrate our approach. I.
