A Modular Framework to Learn Seed Ontologies from Text

Abstract

Ontologies are the basic block of modern knowledge-based systems; however, the effort and expertise required to develop them often prevents their widespread adoption. In this chapter, the authors present a tool for the automatic discovery of basic ontologies—they call them seed ontologies—starting from a corpus of documents related to a specific domain of knowledge. These seed ontologies are not meant for direct use, but they can be used to bootstrap the knowledge acquisition process by providing a selection of relevant terms and fundamental relationships. The tool is modular and it allows the integration of different methods/strategies in the indexing of the corpus, selection of relevant terms, discovery of hierarchies, and other relationships among terms. Like any induction process, ontology learning from text is prone to errors, so the authors do not expect a 100% correct ontology; according to their evaluation the result is closer to 80%, but this should be enough for a domain expert to complete the work with limited effort and in a short time.