Datalog Rewritability of Disjunctive Datalog Programs and its Applications to Ontology Reasoning
This work addresses a problem in knowledge representation and ontology reasoning, offering a tractable extension for non-Horn ontologies, though it appears incremental as it builds on existing datalog frameworks.
The paper tackles the problem of rewriting disjunctive datalog programs into plain datalog, showing that rewritability is equivalent to linear disjunctive programs, and introduces weakly linear disjunctive datalog as a tractable extension for ontology reasoning, with empirical results indicating feasibility in practice.
We study the problem of rewriting a disjunctive datalog program into plain datalog. We show that a disjunctive program is rewritable if and only if it is equivalent to a linear disjunctive program, thus providing a novel characterisation of datalog rewritability. Motivated by this result, we propose weakly linear disjunctive datalog---a novel rule-based KR language that extends both datalog and linear disjunctive datalog and for which reasoning is tractable in data complexity. We then explore applications of weakly linear programs to ontology reasoning and propose a tractable extension of OWL 2 RL with disjunctive axioms. Our empirical results suggest that many non-Horn ontologies can be reduced to weakly linear programs and that query answering over such ontologies using a datalog engine is feasible in practice.