Generic Ontology Learners on Application Domains

Francesca Fallucchi, Maria Teresa Pazienza, 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.
Anthology ID:
L10-1322
Volume:
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Month:
May
Year:
2010
Address:
Valletta, Malta
Venue:
LREC
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Publisher:
European Language Resources Association (ELRA)
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URL:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/466_Paper.pdf
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Cite (ACL):
Francesca Fallucchi, Maria Teresa Pazienza, and Fabio Massimo Zanzotto. 2010. Generic Ontology Learners on Application Domains. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Cite (Informal):
Generic Ontology Learners on Application Domains (Fallucchi et al., LREC 2010)
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http://www.lrec-conf.org/proceedings/lrec2010/pdf/466_Paper.pdf
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