Building Heterogeneous Multi-context Systems by Semantic Bindings

We propose a framework for heterogeneous multi-context systems, in which a special kind of semantic/implicit bridge rules are introduced. Traditional bridge rules in heterogeneous multi-context systems may make the syntax and the semantics of a context more complex, e.g., in the approach of [3] an agent may have to facing a context composed by a description logic systems and a logic program with default negations. In this paper we hide the bridge rules by semantic binding on foreign knowledge fragment, and track the semantic property of a belief/knowledge in one context by a mirror-image of it in the other context. This framework can manage heterogeneous multi-contexts in a simple way, and it keeps the original reasoning properties of the context so that the original reasoning tools are still useful

Semantic Cooperation and Knowledge Reuse by Using Autonomous Ontologies

Several proposals have been put forward to support distributed agent cooperation in the SemanticWeb, by allowing concepts and roles in one ontology be reused in another ontology. In general, these proposals reduce the autonomy of each ontology by defining the semantics of the ontology to depend on the semantics of the other ontologies. We propose a new framework for managing autonomy in a set of cooperating ontologies (or ontology space). In this framework, each language entity concept/role/individual) in an ontology may have its meaning assigned either locally with respect to the semantics of its own ontology, to preserve the autonomy of the ontology, or globally with respect to the semantics of any neighbouring ontology in which it is defined, thus enabling semantic cooperation between multiple ontologies. In this way, each ontology has a “subjective semantics” based on local interpretation and a “foreign semantics” based on semantic binding to neighbouring ontologies. We study the properties of these two semantics and describe the conditions under which entailment and satisfiability are preserved. We also introduce two reasoning mechanisms under this framework: “cautious reasoning” and “brave reasoning”. Cautious reasoning is done with respect to a local ontology and its neighbours (those ontologies in which its entities are defined); brave reasoning is done with respect to the transitive closure of this relationship. This framework is independent of ontology languages. As a case study, for Description Logic ALCN we present two tableau-based algorithms for performing each form of reasonings and prove their correctness

Forgetting Concepts in DL-Lite

Abstract. To support the reuse and combination of ontologies in Semantic Web applications, it is often necessary to obtain smaller ontologies from existing larger ontologies. In particular, applications may require the omission of many terms, e.g., concept names and role names, from an ontology. However, the task of omitting terms from an ontology is challenging because the omission of some terms may affect the relationships between the remaining terms in complex ways. We present the first solution to this problem by adapting the technique of forgetting, previously used in other domains. Specifically, we present a semantic definition of forgetting for description logics in general, which generalizes the standard definition for classical logic. We then introduce algorithms that implement forgetting in both DL-Lite TBoxes and ABoxes, and in DL-Lite knowledge bases. We prove that the algorithms are correct with respect to the semantic definition of forgetting, and that they run in polynomial time.

Forgetting for knowledge bases in DL−Lite

To support the reuse and combination of ontologies in Semantic Web applications, it is often necessary to obtain smaller ontologies from existing larger ontologies. In particular, applications may require the omission of certain terms, e. g., concept names and role names, from an ontology. However, the task of omitting terms from an ontology is challenging because the omission of some terms may affect the relationships between the remaining terms in complex ways.We present the first solution to the problem of omitting concepts and roles from knowledge bases of description logics (DLs) by adapting the technique of forgetting, previously used in other domains. Specifically, we first introduce a model-theoretic definition of forgetting for knowledge bases (both TBoxes and ABoxes) in DL-LiteNbool, which is a non-trivial adaption of the standard definition for classical logic, and show that our model-based forgetting satisfies all major criteria of a rational forgetting operator, which in turn verifies the suitability of our model-based forgetting. We then introduce algorithms that implement forgetting operations in DL-Lite knowledge bases. We prove that the algorithms are correct with respect to the semantic definition of forgetting.We establish a general framework for defining and comparing different definitions of forgetting by introducing a parameterized family of forgetting operators called query-based forgetting operators. In this framework we identify three specific query-based forgetting operators and show that they form a hierarchy. In particular, we show that the model-based forgetting coincides with one of these query-based forgetting operators.Griffith Sciences, School of Information and Communication TechnologyNo Full Tex

Uniform Interpolation for ALC Revisited

The notion of uniform interpolation for description logic ALC has bee