Interaction-based ontology alignment repair with expansion and relaxation

Jérôme Euzenat 1
1 MOEX - Evolution de la connaissance
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Agents may use ontology alignments to communicate when they represent knowledge with different ontologies: alignments help reclassifying objects from one ontology to the other. These alignments may not be perfectly correct, yet agents have to proceed. They can take advantage of their experience in order to evolve alignments: upon communication failure, they will adapt the alignments to avoid reproducing the same mistake. Such repair experiments had been performed in the framework of networks of ontologies related by alignments. They revealed that, by playing simple interaction games, agents can effectively repair random networks of ontologies. Here we repeat these experiments and, using new measures, show that previous results were underestimated. We introduce new adaptation operators that improve those previously considered. We also allow agents to go beyond the initial operators in two ways: they can generate new correspondences when they discard incorrect ones, and they can provide less precise answers. The combination of these modalities satisfy the following properties: (1) Agents still converge to a state in which no mistake occurs. (2) They achieve results far closer to the correct alignments than previously found. (3) They reach again 100% precision and coherent alignments.
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Communication dans un congrès
IJCAI 2017 - 26th International Joint Conference on Artificial Intelligence, Aug 2017, Melbourne, Australia. AAAI Press, Proc. 26th International Joint Conference on Artificial Intelligence (IJCAI), pp.185-191, 2017, 〈https://ijcai-17.org/〉. 〈10.24963/ijcai.2017/27〉
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Dernière modification le : mercredi 11 avril 2018 - 01:52:52

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Jérôme Euzenat. Interaction-based ontology alignment repair with expansion and relaxation. IJCAI 2017 - 26th International Joint Conference on Artificial Intelligence, Aug 2017, Melbourne, Australia. AAAI Press, Proc. 26th International Joint Conference on Artificial Intelligence (IJCAI), pp.185-191, 2017, 〈https://ijcai-17.org/〉. 〈10.24963/ijcai.2017/27〉. 〈hal-01661139v2〉

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