An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in Biomedicine

Author:

Yang Fengqin12,Xing Yuanyuan3,Sun Hongguang12,Sun Tieli14,Chen Siya1

Affiliation:

1. School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China

2. Key Laboratory of Intelligent Information Processing of Jilin Universities, Changchun 130117, China

3. Students’ Affairs Division, Qingdao Technological University, Qingdao 266033, China

4. College of Humanities and Science, Northeast Normal University, Changchun 130117, China

Abstract

Computation of semantic similarity between words for text understanding is a vital issue in many applications such as word sense disambiguation, document categorization, and information retrieval. In recent years, different paradigms have been proposed to compute semantic similarity based on different ontologies and knowledge resources. In this paper, we propose a new similarity measure combining both superconcepts of the evaluated concepts and their common specificity feature. The common specificity feature considers the depth of the Least Common Subsumer (LCS) of two concepts and the depth of the ontology to obtain more semantic evidence. The multiple inheritance phenomenon in a large and complex taxonomy is taken into account by all superconcepts of the evaluated concepts. We evaluate and compare the correlation obtained by our measure with human scores against other existing measures exploiting SNOMED CT as the input ontology. The experimental evaluations show the applicability of the measure on different datasets and confirm the efficiency and simplicity of our proposed measure.

Funder

Jilin Provincial Science and Technology Department of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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