A System for Modal and Deontic Defeasible Reasoning

Defeasible reasoning is a well-established nonmonotonic reasoning approach that has recently been combined with semantic web technologies. This paper describes modal and deontic extensions of defeasible logic, motivated by potential applications for modelling multi-agent systems and policies. It describes a logic metaprogram that captures the underlying intuitions, and outlines an implemented system

Proof Explanation in the DR-DEVICE System

Trust is a vital feature for Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design and implementation of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension

The limits and possibilities of combining Description Logics and Datalog

Description Logics are currently the most used formalisms for building ontologies, and have been proposed as standard languages for the specification of ontologies in the Semantic Web. The problem of adding rules to Description Logics is currently a hot research topic, due to the interest of Semantic Web applications towards the integration of rule-based systems with ontologies. Most of the approaches in this field concern the study of description logic knowledge bases augmented with rules expressed in Datalog and its nonmonotonic extensions. In this talk we present a set of computational results which identify, from the viewpoint of the expressive abilities of the two formalisms, minimal combinations of Description Logics and (nonmonotonic) Datalog in which reasoning is undecidable. Then, based on the above results, we briefly survey some recent proposals for overcoming such expressive limitations. © 2006 IEEE

Equality-friendly well-founded semantics and applications to description logics

We tackle the problem of defining a well-founded semantics (WFS) for Datalog rules with existentially quantified variables in their heads and nega- tions in their bodies. In particular, we provide a WFS for the recent Datalog± family of ontology languages, which covers several important description logics (DLs). To do so, we generalize Datalog± by non-stratified nonmonotonic nega- tion in rule bodies, and we define a WFS for this generalization via guarded fixed point logic. We refer to this approach as equality-friendly WFS, since it has the advantage that it does not make the unique name assumption (UNA); this brings it close to OWL and its profiles as well as typical DLs, which also do not make the UNA. We prove that for guarded Datalog± with negation under the equality- friendly WFS, conjunctive query answering is decidable, and we provide precise complexity results for this problem. From these results, we obtain precise defi- nitions of the standard WFS extensions of EL and of members of the DL-Lite family, as well as corresponding complexity results for query answering

Inline Evaluation of Hybrid Knowledge Bases PhD Description

Abstract. The deployment of knowledge representation formalisms to the Web has created the need for hybrid formalisms that combine heterogeneous knowledge bases. The aim of this research is to improve the reasoning efficiency over hybrid knowledge bases (KBs). The traditional way of reasoning over hybrid KBs is to use different underlying reasoners to access the different data sources, which causes overhead. To remedy this, we propose a new strategy, called inline evaluation, which compiles the whole hybrid KB into a new KB using only one single formalism. Hence we can use a single reasoner to do the reasoning tasks, and improve the efficiency of hybrid reasoning

Integrating rules and ontologies in the first-order stable model semantics (preliminary report

Abstract. We present an approach to integrating rules and ontologies on the basis of the first-order stable model semantics defined by Ferraris, Lee and Lifschitz. We show that a few existing integration proposals can be uniformly related to the first-order stable model semantics.

Inductive Logic Programming in Databases: from Datalog to DL+log

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e. the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of KR aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework DL+log. We illustrate the application scenarios by means of examples. Keywords: Inductive Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid Knowledge Representation and Reasoning Systems. Note: To appear in Theory and Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables