A review of semantic similarity approach for multiple ontologies
Measuring semantic similarity between concepts is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. Many method...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Inderscience Enterprises Ltd.
2018
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/5549/1/AJ%202018%20%28576%29.pdf |
| _version_ | 1796869219074179072 |
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| author | Omar, Nurul Aswa Kasim, Shahreen Md Fudzee, Mohd Farhan |
| author_facet | Omar, Nurul Aswa Kasim, Shahreen Md Fudzee, Mohd Farhan |
| author_sort | Omar, Nurul Aswa |
| collection | UTHM |
| description | Measuring semantic similarity between concepts is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. Many methods have been proposed. This paper contains a review on the state of art approaches including structure-based approach, information content-based approach, feature-based approach and hybrid-based approach. We also discussed the similarity according to their advantages, disadvantages and issues related to multiple ontologies. Besides that, we also concentrated on methods in feature-based approach which we will be using as a mechanism to measure the similarity for multiple ontologies |
| first_indexed | 2024-03-05T21:51:34Z |
| format | Article |
| id | uthm.eprints-5549 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:51:34Z |
| publishDate | 2018 |
| publisher | Inderscience Enterprises Ltd. |
| record_format | dspace |
| spelling | uthm.eprints-55492022-01-13T08:04:29Z http://eprints.uthm.edu.my/5549/ A review of semantic similarity approach for multiple ontologies Omar, Nurul Aswa Kasim, Shahreen Md Fudzee, Mohd Farhan QA71-90 Instruments and machines Measuring semantic similarity between concepts is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. Many methods have been proposed. This paper contains a review on the state of art approaches including structure-based approach, information content-based approach, feature-based approach and hybrid-based approach. We also discussed the similarity according to their advantages, disadvantages and issues related to multiple ontologies. Besides that, we also concentrated on methods in feature-based approach which we will be using as a mechanism to measure the similarity for multiple ontologies Inderscience Enterprises Ltd. 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5549/1/AJ%202018%20%28576%29.pdf Omar, Nurul Aswa and Kasim, Shahreen and Md Fudzee, Mohd Farhan (2018) A review of semantic similarity approach for multiple ontologies. Int. J. Information and Decision Sciences, 10 (3). pp. 212-221. ISSN 1756-7017 http://dx.doi.org/10.1504/IJIDS.2018.093921 |
| spellingShingle | QA71-90 Instruments and machines Omar, Nurul Aswa Kasim, Shahreen Md Fudzee, Mohd Farhan A review of semantic similarity approach for multiple ontologies |
| title | A review of semantic similarity approach for multiple ontologies |
| title_full | A review of semantic similarity approach for multiple ontologies |
| title_fullStr | A review of semantic similarity approach for multiple ontologies |
| title_full_unstemmed | A review of semantic similarity approach for multiple ontologies |
| title_short | A review of semantic similarity approach for multiple ontologies |
| title_sort | review of semantic similarity approach for multiple ontologies |
| topic | QA71-90 Instruments and machines |
| url | http://eprints.uthm.edu.my/5549/1/AJ%202018%20%28576%29.pdf |
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