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An ILP Perspective on the Semantic Web.

Francesca A. Lisi, Floriana Esposito

Open publisher page 5 citations

Abstract

Building rules on top of ontologies is the goal of the logical layer of the Semantic Web. The system originally conceived for hybrid Knowledge Representation and Reasoning (KR&R), has been very recently mentioned as the blueprint for well-founded Semantic Web rule mark-up languages. It integrates the description logic and the function-free Horn clausal language Datalog. In this paper we provide a framework for learning Semantic Web rules which adopts Inductive Logic Programming (ILP) as methodological apparatus and as KR&R setting. In this framework inductive hypotheses are represented as constrained Datalog clauses, organized according to the relation, and evaluated against observations by means of coverage relations. The

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What this paper is about

Building rules on top of ontologies is the goal of the logical layer of the Semantic Web. The system originally conceived for hybrid Knowledge Representation and Reasoning (KR&R), has been very recently mentioned as the blueprint for well-founded Semantic Web rule mark-up languages. It integrates the description logic and the function-free Horn clausal language Datalog. In this paper we provide a framework for learning Semantic Web rules which adopts Inductive Logic Programming (ILP) as methodological apparatus and as KR&R setting. In this framework inductive hypotheses are represented as constrained Datalog clauses, organized according to the relation, and evaluated against observations by means of coverage relations. The

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Available abstract

Building rules on top of ontologies is the goal of the logical layer of the Semantic Web. The system originally conceived for hybrid Knowledge Representation and Reasoning (KR&R), has been very recently mentioned as the blueprint for well-founded Semantic Web rule mark-up languages. It integrates the description logic and the function-free Horn clausal language Datalog. In this paper we provide a framework for learning Semantic Web rules which adopts Inductive Logic Programming (ILP) as methodological apparatus and as KR&R setting. In this framework inductive hypotheses are represented as constrained Datalog clauses, organized according to the relation, and evaluated against observations by means of coverage relations. The

Key concepts: Datalog, Inductive logic programming, Computer science, Programming language, Semantic Web Rule Language, Semantic Web, Knowledge representation and reasoning, Scope (computer science)

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