2019 IJCAI IJCAI 2019

Finding Justifications by Approximating Core for Large-scale Ontologies

Abstract

Finding justifications for an entailment is one of the major missions in the field of ontology research. Recent advances on finding justifications w.r.t. the light-weight description logics focused on encoding this problem into a propositional formula, and using SAT-based techniques to enumerate all MUSes (minimally unsatisfiable subformulas). It's necessary to import more optimized techniques into finding justifications as emergence of large-scale real-world ontologies. In this paper, we propose a new strategy which introduce local search(in short, LS) technique to compute the approximating core before extracting an exact MUS. Although it is based on a heuristic and LS, such technique is complete in the sense that it always delivers a MUS for any unsatisfiable SAT instance. Our method will find the justifications for large-scale ontologies more effectively.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Knowledge & Reasoning
🧭 Keyword Pioneer — justification extraction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy