Approximation analysis of ontology learning algorithm in linear combination setting
Abstract In the past ten years, researchers have always attached great importance to the application of ontology to its relevant specific fields. At the same time, applying learning algorithms to many ontology algorithms is also a hot topic. For example, ontology learning technology and knowledge ar...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-06-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13677-020-00173-y |
| _version_ | 1818937092291428352 |
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| author | Wei Gao Yaojun Chen |
| author_facet | Wei Gao Yaojun Chen |
| author_sort | Wei Gao |
| collection | DOAJ |
| description | Abstract In the past ten years, researchers have always attached great importance to the application of ontology to its relevant specific fields. At the same time, applying learning algorithms to many ontology algorithms is also a hot topic. For example, ontology learning technology and knowledge are used in the field of semantic retrieval and machine translation. The field of discovery and information systems can also be integrated with ontology learning techniques. Among several ontology learning tricks, multi-dividing ontology learning is the most popular one which proved to be in high efficiency for the similarity calculation of tree structure ontology. In this work, we study the multi-dividing ontology learning algorithm from the mathematical point of view, and an approximation conclusion is presented under the linear representation assumption. The theoretical result obtained here has constructive meaning for the similarity calculation and concrete engineering applications of tree-shaped ontologies. Finally, linear combination multi-dividing ontology learning is applied to university ontologies and mathematical ontologies, and the experimental results imply that the higher efficiency of the proposed approach in actual applications. |
| first_indexed | 2024-12-20T05:46:28Z |
| format | Article |
| id | doaj.art-4177622194ed4ca7a1b093fcc37d79b4 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-12-20T05:46:28Z |
| publishDate | 2020-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-4177622194ed4ca7a1b093fcc37d79b42022-12-21T19:51:18ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2020-06-019111010.1186/s13677-020-00173-yApproximation analysis of ontology learning algorithm in linear combination settingWei Gao0Yaojun Chen1School of Information Science and Technology, Yunnan Normal UniversityDepartment of Mathematics, Nanjing UniversityAbstract In the past ten years, researchers have always attached great importance to the application of ontology to its relevant specific fields. At the same time, applying learning algorithms to many ontology algorithms is also a hot topic. For example, ontology learning technology and knowledge are used in the field of semantic retrieval and machine translation. The field of discovery and information systems can also be integrated with ontology learning techniques. Among several ontology learning tricks, multi-dividing ontology learning is the most popular one which proved to be in high efficiency for the similarity calculation of tree structure ontology. In this work, we study the multi-dividing ontology learning algorithm from the mathematical point of view, and an approximation conclusion is presented under the linear representation assumption. The theoretical result obtained here has constructive meaning for the similarity calculation and concrete engineering applications of tree-shaped ontologies. Finally, linear combination multi-dividing ontology learning is applied to university ontologies and mathematical ontologies, and the experimental results imply that the higher efficiency of the proposed approach in actual applications.http://link.springer.com/article/10.1186/s13677-020-00173-yOntologyLearning algorithmMulti-dividing settingLinear combination |
| spellingShingle | Wei Gao Yaojun Chen Approximation analysis of ontology learning algorithm in linear combination setting Journal of Cloud Computing: Advances, Systems and Applications Ontology Learning algorithm Multi-dividing setting Linear combination |
| title | Approximation analysis of ontology learning algorithm in linear combination setting |
| title_full | Approximation analysis of ontology learning algorithm in linear combination setting |
| title_fullStr | Approximation analysis of ontology learning algorithm in linear combination setting |
| title_full_unstemmed | Approximation analysis of ontology learning algorithm in linear combination setting |
| title_short | Approximation analysis of ontology learning algorithm in linear combination setting |
| title_sort | approximation analysis of ontology learning algorithm in linear combination setting |
| topic | Ontology Learning algorithm Multi-dividing setting Linear combination |
| url | http://link.springer.com/article/10.1186/s13677-020-00173-y |
| work_keys_str_mv | AT weigao approximationanalysisofontologylearningalgorithminlinearcombinationsetting AT yaojunchen approximationanalysisofontologylearningalgorithminlinearcombinationsetting |