Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm
The development of intelligent transportation systems (ITSs) faces the challenge of integrating data from multiple unrelated sources. As one of the core technologies of knowledge integration in ITS, an ontology typically provides a normative definition of transportation domain that can be used as a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/4439861 |
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| _version_ | 1832568481859502080 |
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| author | Xingsi Xue Haolin Wang Jie Zhang Yikun Huang Mengting Li Hai Zhu |
| author_facet | Xingsi Xue Haolin Wang Jie Zhang Yikun Huang Mengting Li Hai Zhu |
| author_sort | Xingsi Xue |
| collection | DOAJ |
| description | The development of intelligent transportation systems (ITSs) faces the challenge of integrating data from multiple unrelated sources. As one of the core technologies of knowledge integration in ITS, an ontology typically provides a normative definition of transportation domain that can be used as a reference for information integration. However, due to the subjectivity of domain experts, a concept may be expressed in multiple ways, yielding the ontology heterogeneity problem. Ontology matching (OM) is an effective method of addressing it, which is of help to further realize the mutual communication between the ontology-based ITSs. In this work, we first propose to use Word2Vec to model the entities in vector space and calculate their similarity values. Then, a stable marriage-based alignment extraction algorithm is presented to determine high-quality alignment. In the experiment, the performance of the proposal is tested by using the benchmark track of OAEI and real transportation ontologies. The experimental results show that our approach is able to obtain higher quality alignment results than OAEI’s participants and other state-of-the-art ontology matching techniques. |
| format | Article |
| id | doaj-art-26e11a0cf802465b8115c898f50d640b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-26e11a0cf802465b8115c898f50d640b2025-02-03T00:58:56ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/44398614439861Matching Transportation Ontologies with Word2Vec and Alignment Extraction AlgorithmXingsi Xue0Haolin Wang1Jie Zhang2Yikun Huang3Mengting Li4Hai Zhu5Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian 350118, ChinaIntelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian 350118, ChinaSchool of Computer Science and Engineering, Yulin Normal University, Yulin, Guanxi 537000, ChinaConcord University College Fujian Normal University, Fuzhou, Fujian 350117, ChinaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian 350118, ChinaSchool of Network Engineering, Zhoukou Normal University, Zhoukou, Henan 466001, ChinaThe development of intelligent transportation systems (ITSs) faces the challenge of integrating data from multiple unrelated sources. As one of the core technologies of knowledge integration in ITS, an ontology typically provides a normative definition of transportation domain that can be used as a reference for information integration. However, due to the subjectivity of domain experts, a concept may be expressed in multiple ways, yielding the ontology heterogeneity problem. Ontology matching (OM) is an effective method of addressing it, which is of help to further realize the mutual communication between the ontology-based ITSs. In this work, we first propose to use Word2Vec to model the entities in vector space and calculate their similarity values. Then, a stable marriage-based alignment extraction algorithm is presented to determine high-quality alignment. In the experiment, the performance of the proposal is tested by using the benchmark track of OAEI and real transportation ontologies. The experimental results show that our approach is able to obtain higher quality alignment results than OAEI’s participants and other state-of-the-art ontology matching techniques.http://dx.doi.org/10.1155/2021/4439861 |
| spellingShingle | Xingsi Xue Haolin Wang Jie Zhang Yikun Huang Mengting Li Hai Zhu Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm Journal of Advanced Transportation |
| title | Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm |
| title_full | Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm |
| title_fullStr | Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm |
| title_full_unstemmed | Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm |
| title_short | Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm |
| title_sort | matching transportation ontologies with word2vec and alignment extraction algorithm |
| url | http://dx.doi.org/10.1155/2021/4439861 |
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