A Similarity-Based Concepts Mapping Method between Ontologies

Jie LIU; Linlin QIN; Jing GAO; Aidong ZHANG

doi:10.1587/transinf.2014EDP7188

A Similarity-Based Concepts Mapping Method between Ontologies

Jie LIU, Linlin QIN, Jing GAO, Aidong ZHANG

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Ontology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.

Publication: IEICE TRANSACTIONS on Information Vol.E98-D No.5 pp.1062-1072

Publication Date: 2015/05/01

Publicized: 2015/01/26

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2014EDP7188

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

Authors

Jie LIU
  Capital Normal University
Linlin QIN
  Capital Normal University
Jing GAO
  State University of New York
Aidong ZHANG
  State University of New York

Keyword

ontology mapping, similarity aggregation, SVM

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Jie LIU, Linlin QIN, Jing GAO, Aidong ZHANG, "A Similarity-Based Concepts Mapping Method between Ontologies" in IEICE TRANSACTIONS on Information, vol. E98-D, no. 5, pp. 1062-1072, May 2015, doi: 10.1587/transinf.2014EDP7188.
Abstract: Ontology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7188/_p

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@ARTICLE{e98-d_5_1062,
author={Jie LIU, Linlin QIN, Jing GAO, Aidong ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Similarity-Based Concepts Mapping Method between Ontologies},
year={2015},
volume={E98-D},
number={5},
pages={1062-1072},
abstract={Ontology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.},
keywords={},
doi={10.1587/transinf.2014EDP7188},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - A Similarity-Based Concepts Mapping Method between Ontologies
T2 - IEICE TRANSACTIONS on Information
SP - 1062
EP - 1072
AU - Jie LIU
AU - Linlin QIN
AU - Jing GAO
AU - Aidong ZHANG
PY - 2015
DO - 10.1587/transinf.2014EDP7188
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2015
AB - Ontology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.
ER -