Summary of the paper

Title Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse
Authors Artemis Parvizi, Matt Kohl, Meritxell Gonzàlez and Roser Saurí
Abstract The Dictionaries division at Oxford University Press (OUP) is aiming to model, integrate, and publish lexical content for 100 languages focussing on digitally under-represented languages. While there are multiple ontologies designed for linguistic resources, none had adequate features for meeting our requirements, chief of which was the capability to losslessly capture diverse features of many different languages in a dictionary format, while supplying a framework for inferring relations like translation, derivation, etc., between the data. Building on valuable features of existing models, and working with OUP monolingual and bilingual dictionary datasets, we have designed and implemented a new linguistic ontology. The ontology has been reviewed by a number of computational linguists, and we are working to move more dictionary data into it. We have also developed APIs to surface the linked data to dictionary websites.
Topics Language Modelling, Ontologies, Semantic Web
Full paper Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse
Bibtex @InProceedings{PARVIZI16.523,
  author = {Artemis Parvizi and Matt Kohl and Meritxell Gonzàlez and Roser Saurí},
  title = {Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portorož, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
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