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Title Generic Ontology Learners on Application Domains
Authors Francesca Fallucchi, Maria Teresa Pazienza and Fabio Massimo Zanzotto
Abstract In ontology learning from texts, we have ontology-rich domains where we have large structured domain knowledge repositories or we have large general corpora with large general structured knowledge repositories such as WordNet (Miller, 1995). Ontology learning methods are more useful in ontology-poor domains. Yet, in these conditions, these methods have not a particularly high performance as training material is not sufficient. In this paper we present an LSP ontology learning method that can exploit models learned from a generic domain to extract new information in a specific domain. In our model, we firstly learn a model from training data and then we use the learned model to discover knowledge in a specific domain. We tested our model adaptation strategy using a background domain that is applied to learn the isa networks in the Earth Observation Domain as a specific domain. We will demonstrate that our method captures domain knowledge better than other generic models: our model better captures what is expected by domain experts than a baseline method based only on WordNet. This latter is better correlated with non-domain annotators asked to produce the ontology for the specific domain.
Topics Statistical and machine learning methods, Knowledge Discovery/Representation, Information Extraction, Information Retrieval
Full paper Generic Ontology Learners on Application Domains
Slides Generic Ontology Learners on Application Domains
Bibtex @InProceedings{FALLUCCHI10.466,
  author = {Francesca Fallucchi and Maria Teresa Pazienza and Fabio Massimo Zanzotto},
  title = {Generic Ontology Learners on Application Domains},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
 }
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