Abstract
We study the closely related problems of rewriting disjunctive datalog programs and non-Horn DL ontologies into plain datalog programs that entail the same facts for every dataset. We first propose the class of markable disjunctive datalog programs, which is efficiently recognisable and admits polynomial rewritings into datalog. Markability naturally extends to \(\mathcal{SHI}\) ontologies, and markable ontologies admit (possibly exponential) datalog rewritings. We then turn our attention to resolution-based rewriting techniques. We devise an enhanced rewriting procedure for disjunctive datalog, and propose a second class of \(\mathcal{SHI}\) ontologies that admits exponential datalog rewritings via resolution. Finally, we focus on conjunctive query answering over disjunctive datalog programs. We identify classes of queries and programs that admit datalog rewritings and study the complexity of query answering in this setting. We evaluate the feasibility of our techniques over a large corpus of ontologies, with encouraging results.
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Kaminski, M., Nenov, Y., Cuenca Grau, B. (2014). Computing Datalog Rewritings for Disjunctive Datalog Programs and Description Logic Ontologies. In: Kontchakov, R., Mugnier, ML. (eds) Web Reasoning and Rule Systems. RR 2014. Lecture Notes in Computer Science, vol 8741. Springer, Cham. https://doi.org/10.1007/978-3-319-11113-1_6
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DOI: https://doi.org/10.1007/978-3-319-11113-1_6
Publisher Name: Springer, Cham
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