On the Web Ontology Rule Language OWL 2 RL

Abstract

Abstract. It is known that the OWL 2 RL Web Ontology Language Profile has PTime data complexity and can be translated into Datalog. However, the result of translation may consist of a Datalog program and a set of constraints in the form of negative clauses. Therefore, a knowledge base in OWL 2 RL may be unsatisfiable. In the current paper we first identify a maximal fragment of OWL 2 RL, called OWL 2 RL + , with the property that every knowledge base expressed in OWL 2 RL + can be translated to a Datalog program and hence is satisfiable. We then propose some extensions of OWL 2 RL and OWL 2 RL + that still have PTime data complexity.

A bisimulation-based method of concept learning for knowledge bases in description logics

“…An information system in a DL is a finite interpretation in that logic. It can be given explicitly or specified somehow, e.g., by a knowledge base in the rule language OWL 2 RL + [19] (using the standard semantics) or WORL [20] (using the well-founded semantics) or SWORL [20] (using the stratified semantics) or by an acyclic knowledge base [9] (using the closed world assumption). Thus, both the works [8], [9] use the third mentioned setting.…”

On the efficiency of query-subquery nets

“…In the near future, we plan to develop a more efficient framework based on QSQN by incorporating recursion tail elimination and apply our method to Datalog-like rule languages of Semantic Web [5,6].…”

A Generalized QSQR Evaluation Method for Horn Knowledge Bases

“…As shown in Section 5, in comparison with the tuple-oriented depth-first approach, it significantly reduces the number of accesses to secondary storage. As future work, we intend to further improve the method, for example, by using some ideas of the QoSaQ and seminaive (bottom-up) evaluation methods, and apply our method for Datalog-like rule languages of Semantic Web [Cao et al 2011a;2011b].…”