State-of-the-art multilingual ontology matchers use machine translation to reduce the problem to the monolingual case. We investi- gate an alternative, self-contained solution based on semantic matching where labels are parsed by multilingual natural language processing and then matched using a language-independent knowledge base acting as an interlingua. As the method relies on the availability of domain vocabu- laries in the languages supported, matching and vocabulary enrichment become joint, mutually reinforcing tasks. In particular, we propose a vo- cabulary enrichment method that uses the matcher’s output to detect and generate missing items semi-automatically. Vocabularies developed in this manner can then be reused for other domain-specific natural lan- guage understanding tasks.
|Title of host publication||Proceedings of the 10th Workshop on Ontology Matching|
|Number of pages||12|
|Publication status||Published - 12 Oct 2015|
|Name||CEUR Workshop Proceedings|
- Artificial Intelligence
- Computer Science (miscellaneous)