A Practical Approach To Forgetting In Description Logics With Nominals

THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE(2020)

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摘要
This paper investigates the problem of forgetting in description logics with nominals. In particular, we develop a practical method for forgetting concept and role names from ontologies specified in the description logic ALCO, extending the basic ALC with nominals. The method always terminates, and is sound in the sense that the forgetting solution computed by the method has the same logical consequences with the original ontology. The method is so far the only approach to deductive forgetting in description logics with nominals. An evaluation of a prototype implementation shows that the method achieves a significant speed-up and notably better success rates than the LETHE tool which performs deductive forgetting for ALC-ontologies. Compared to FAME, a semantic forgetting tool for ALCOIH-ontologies, better success rates are attained. From the perspective of ontology engineering this is very useful, as it provides ontology curators with a powerful tool to produce views of ontologies.
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