An Incremental Tri-Partite Approach To Ontology Learning
José Iria, Christopher A. Brewster, Fabio Ciravegna, Yorick Alexander Wilks · 2006
In this paper we present a new approach to ontology learning.Its basis lies in a dynamic and iterative view of knowledge acquisition for ontologies.The Abraxas approach is founded on three resources, a set of texts, a set of learning patterns and a set of ontological triples, each of which must remain in equilibrium.As events occur which disturb this equilibrium various actions are triggered to re-establish a balance between the resources.Such events include acquisition of a further text from external resources such as the Web or the addition of ontological triples to the ontology.We develop the concept of a knowledge gap between the coverage of an ontology and the corpus of texts as a measure triggering actions.We present an overview of the algorithm and its functionalities.