Generic Ontology Learners on Application Domains
Francesca Fallucchi, Maria Teresa Pazienza, Fabio Massimo Zanzotto · 2010
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 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 general domain traning data and then we use the learned model to discover the relation of two words in specific domains. We tested our model adaptation strategy using a background domain that is applied to learn the isa networks in a specific domain, i.e., the Earth Observation Domain. 1.