Extracting Hyponymy Relations from Domain-Specific Free Texts

Domain-specific ontologies have shown their powerful usefulness in many application areas, such as semantic web, information sharing, and natural language processing. However, manually building of domain ontologies still remains a tedious and cumbersome task. Hyponymy is a core component of domain-specific ontologies. In this paper, we propose three symbolic learning methods, which are integrated together to extract hyponymies from un-annotated domain-specific Chinese free texts. The three symbolic learning methods include seed-driven learning, pattern-mediated learning, and term composition based learning. Experimental results show that the algorithm is adequate to extracting the hyponymies from unstructured domain-specific Chinese corpus.