Reasoning strategies for semantic Web rule languages

Abstract

Dealing with data in open, distributed environments is an increasingly important problem today. The processing of heterogeneous data in formats such as RDF is still being researched. Using rules and rule engines is one technique that is being used. In doing so, the problem of handling heterogeneous rules from multiple sources becomes important. Over the course of this thesis, I wrote several kinds of reasoners including backward, forward, and hybrid reasoners for RDF rule languages. These were used for a variety of problems and data in a wide range of settings for solving real world problems. During my investigations, I learned several interesting problems of RDF. First, simply making the term space big and well names paced and the language low enough expressivity did not make computation necessarily easier. Next, checking proofs in an RDF environment proved to be hard because the basic features of RDF that make it possible for it to represent heterogeneous data effectively make proofs difficult. Further work is needed to see if some of these problems can be mitigated. Though rules are useful, using rules correctly and efficiently for processing RDF data proved to be difficult.