The More the Merrier: Combining Properties for ABox Abduction under Repair Semantics for ELbot
For researchers in knowledge representation and reasoning, this work extends the understanding of abduction under repair semantics by showing that combining multiple properties can be achieved without extra computational cost.
The paper investigates ABox abduction under repair semantics for EL_bot, focusing on hypotheses that satisfy multiple desirable properties or combine a property with an optimality criterion. The main finding is that requiring additional properties often does not increase complexity.
Abduction is a central approach to explain missing entailments from a knowledge base by providing a hypothesis, that would, if added to the knowledge base, make the missing entailment become true. Abduction under repair semantics has recently been investigated in detail, where several desirable properties and optimality criteria were considered, such as signature-restrictions and minimality in size and of introduced conflicts. Naturally, hypotheses that satisfy more than one of these properties or combine a property with an optimality criterion would be even more desirable for applications. So far, such hypotheses have not been investigated in the literature. In the present paper, we consider the ABox abduction problem for hypotheses satisfying more than one property or additional optimality criteria, for EL_bot under brave and AR semantics. Our main observation is that often requiring additional properties for hypotheses does not lead to an increase of complexity.