Resolution-based rewriting for Horn-$$\mathcal {SHIQ}$$SHIQ ontologies

Abstract

An important approach to query answering over description logic (DL) ontologies is via rewriting the input ontology and query into languages such as (disjunctive) datalog, for which scalable data saturation systems exist. This approach has been studied for DLs of different expressivities such as DL-Lite, $$\mathcal {ELHI}$$ and Horn-$$\mathcal {SHIQ}$$. When it comes to expressive languages resolution is an important technique that can be applied to obtain the rewritings. This is mainly because it allows for the design of general-purpose algorithms that can be easily lifted to support languages of high expressivity. In the current work we present an efficient resolution-based rewriting algorithm tailor-made for the expressive DL language Horn-$$\mathcal {SHIQ}$$. Our algorithm avoids performing many unnecessary inferences, which is one of the main problems of resolution-based algorithms. This is achieved by careful analysis of the complex axioms structure supported in Horn-$$\mathcal {SHIQ}$$. Moreover, we have implemented the proposed algorithm and obtained very encouraging results when conducting extensive experimental evaluation.