Exploiting Transitivity in Probabilistic Models for Ontology Learning
Francesca Fallucchi, Fabio Massimo Zanzotto · IGI Global eBooks · 2012
The authors propose probabilistic models for learning ontologies that expand existing ontologies taking into account both corpus-extracted evidence and the structure of the generated ontologies. The model exploits structural properties of target relations such as transitivity during learning. They then propose two extensions of the probabilistic models: a model for learning from a generic domain that can be exploited to extract new information in a specific domain and an incremental ontology learning system that puts human validations in the learning loop. This latter provides a graphical user interface and a human-computer interaction workflow supporting the incremental leaning loop.