An Automatic and Clause-Based Approach to Learn Relations for Ontologies

摘要

Ontology learning from text is one of the knowledge acquisition processes that facilitates construction of ontology. Considerable research is being done on learning concepts and relations, especially on acquiring semantic relations between concepts for a specific domain. However, more of the research contributions are in learning either taxonomic relations or semantic relations but not in both. Even those few research works that address learning of both types of relations deal with simple sentences only resulting in low recall value. Further, these approaches are semi-automatic, which require either user's feedback or domain expert's knowledge. In this paper, we propose a single framework that is automatic and domain-independent that helps in learning both taxonomic and non-taxonomic relations. We have developed a clause-based approach that automatically extracts the relations for concepts from unstructured text documents. Our approach is capable of handling complex sentences by identifying hidden triples present in the sentences. We have evaluated our methodology of relation learning for the concepts specified by AGROVOC and Open Directory Project using a corpus of web documents. The precision, recall and Fl-measure of our method were observed to be considerably higher than those of state-of-the-art methodologies for relation learning.