关键词: 本体学习, 概念抽取, 抑郁症, 对数似然比, 领域关联函数

Abstract: It has a significant guiding significance for developing intelligent?auxiliary diagnosis and treatment of depression to develop automatically ontology learning technology of depression drug ontology based on mass biomedicine literatures. Concepts extraction has an effect on learning relations and axioms, and then decides the quality of ontology learning. However, existing algorithms are only suitable for general fields, not for special and fine grit fields. This paper proposes a method of concepts extraction from depression drug field, which employs log-likelihood ratio and domain correlation function based on traditional domain relevance and domain consensus. The experimental results show that the method can reduce the impact on concepts extraction caused by other related fields of depression, and improve the calculation of domain membership degree for low-frequency terms. Compared with others, the precise and recall has been greatly improved.

Key words: ontology learning, concepts extraction, depression, log-likelihood ratio, domain correlation function