A Domain-Independent Ontology Learning Method Based on Transfer Learning
Ontology plays a critical role in knowledge engineering and knowledge graphs (KGs). However, building ontology is still a nontrivial task. Ontology learning aims at generating domain ontologies from various kinds of resources by natural language processing and machine learning techniques. One major...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/16/1911 |
| _version_ | 1797524122207518720 |
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| author | Kai Xie Chao Wang Peng Wang |
| author_facet | Kai Xie Chao Wang Peng Wang |
| author_sort | Kai Xie |
| collection | DOAJ |
| description | Ontology plays a critical role in knowledge engineering and knowledge graphs (KGs). However, building ontology is still a nontrivial task. Ontology learning aims at generating domain ontologies from various kinds of resources by natural language processing and machine learning techniques. One major challenge of ontology learning is reducing labeling work for new domains. This paper proposes an ontology learning method based on transfer learning, namely TF-Mnt, which aims at learning knowledge from new domains that have limited labeled data. This paper selects Web data as the learning source and defines various features, which utilizes abundant textual information and heterogeneous semi-structured information. Then, a new transfer learning model TF-Mnt is proposed, and the parameters’ estimation is also addressed. Although there exist distribution differences of features between two domains, TF-Mnt can measure the relevance by calculating the correlation coefficient. Moreover, TF-Mnt can efficiently transfer knowledge from the source domain to the target domain and avoid negative transfer. Experiments in real-world datasets show that TF-Mnt achieves promising learning performance for new domains despite the small number of labels in it, by learning knowledge from a proper existing domain which can be automatically selected. |
| first_indexed | 2024-03-10T08:52:51Z |
| format | Article |
| id | doaj.art-a5a875f35853417db24daf20c9ceeda7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T08:52:51Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a5a875f35853417db24daf20c9ceeda72023-11-22T07:24:24ZengMDPI AGElectronics2079-92922021-08-011016191110.3390/electronics10161911A Domain-Independent Ontology Learning Method Based on Transfer LearningKai Xie0Chao Wang1Peng Wang2School of Computer Science and Engineering, Southeast University, Nanjing 211189, ChinaSchool of Computer Science and Engineering, Southeast University, Nanjing 211189, ChinaSchool of Computer Science and Engineering, Southeast University, Nanjing 211189, ChinaOntology plays a critical role in knowledge engineering and knowledge graphs (KGs). However, building ontology is still a nontrivial task. Ontology learning aims at generating domain ontologies from various kinds of resources by natural language processing and machine learning techniques. One major challenge of ontology learning is reducing labeling work for new domains. This paper proposes an ontology learning method based on transfer learning, namely TF-Mnt, which aims at learning knowledge from new domains that have limited labeled data. This paper selects Web data as the learning source and defines various features, which utilizes abundant textual information and heterogeneous semi-structured information. Then, a new transfer learning model TF-Mnt is proposed, and the parameters’ estimation is also addressed. Although there exist distribution differences of features between two domains, TF-Mnt can measure the relevance by calculating the correlation coefficient. Moreover, TF-Mnt can efficiently transfer knowledge from the source domain to the target domain and avoid negative transfer. Experiments in real-world datasets show that TF-Mnt achieves promising learning performance for new domains despite the small number of labels in it, by learning knowledge from a proper existing domain which can be automatically selected.https://www.mdpi.com/2079-9292/10/16/1911ontology learningtransfer learningontology |
| spellingShingle | Kai Xie Chao Wang Peng Wang A Domain-Independent Ontology Learning Method Based on Transfer Learning Electronics ontology learning transfer learning ontology |
| title | A Domain-Independent Ontology Learning Method Based on Transfer Learning |
| title_full | A Domain-Independent Ontology Learning Method Based on Transfer Learning |
| title_fullStr | A Domain-Independent Ontology Learning Method Based on Transfer Learning |
| title_full_unstemmed | A Domain-Independent Ontology Learning Method Based on Transfer Learning |
| title_short | A Domain-Independent Ontology Learning Method Based on Transfer Learning |
| title_sort | domain independent ontology learning method based on transfer learning |
| topic | ontology learning transfer learning ontology |
| url | https://www.mdpi.com/2079-9292/10/16/1911 |
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