An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in Biomedicine
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 differe...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/305369 |
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doaj-ef006c90140646d982031e7f7ac7471f2020-11-24T21:18:37ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/305369305369An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in BiomedicineFengqin Yang0Yuanyuan Xing1Hongguang Sun2Tieli Sun3Siya Chen4School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, ChinaStudents’ Affairs Division, Qingdao Technological University, Qingdao 266033, ChinaSchool of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, ChinaSchool of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, ChinaSchool of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, ChinaComputation 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.http://dx.doi.org/10.1155/2015/305369 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fengqin Yang Yuanyuan Xing Hongguang Sun Tieli Sun Siya Chen |
spellingShingle |
Fengqin Yang Yuanyuan Xing Hongguang Sun Tieli Sun Siya Chen An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in Biomedicine Mathematical Problems in Engineering |
author_facet |
Fengqin Yang Yuanyuan Xing Hongguang Sun Tieli Sun Siya Chen |
author_sort |
Fengqin Yang |
title |
An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in Biomedicine |
title_short |
An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in Biomedicine |
title_full |
An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in Biomedicine |
title_fullStr |
An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in Biomedicine |
title_full_unstemmed |
An Ontology-Based Semantic Similarity Measure Considering Multi-Inheritance in Biomedicine |
title_sort |
ontology-based semantic similarity measure considering multi-inheritance in biomedicine |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2015/305369 |
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