WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs
With the further development of knowledge graphs, many weighted knowledge graphs (WKGs) have been published and greatly promote various applications. However, current deterministic knowledge graph embedding algorithms cannot encode weighted knowledge graphs well. This paper gives a promising framewo...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10124725/ |
| _version_ | 1797820024892686336 |
|---|---|
| author | Kong Wei Kun Xin Liu Teeradaj Racharak Guanqun Sun Jianan Chen Qiang Ma Le-Minh Nguyen |
| author_facet | Kong Wei Kun Xin Liu Teeradaj Racharak Guanqun Sun Jianan Chen Qiang Ma Le-Minh Nguyen |
| author_sort | Kong Wei Kun |
| collection | DOAJ |
| description | With the further development of knowledge graphs, many weighted knowledge graphs (WKGs) have been published and greatly promote various applications. However, current deterministic knowledge graph embedding algorithms cannot encode weighted knowledge graphs well. This paper gives a promising framework WeExt that can extend deterministic knowledge graph embedding models to enable them to learn weighted knowledge graph embeddings. In addtion, we introduce weighted link prediction to evaluate the weighted knowledge graph embedding models’ performance on completing WKGs. Finally, we give concrete implementation of WeExt based on two translational distance models and two semantic matching models. Our experimental results show the proposed framework achieves promising performance in link prediction, weight prediction, and weighted link prediction. |
| first_indexed | 2024-03-13T09:31:18Z |
| format | Article |
| id | doaj.art-43e06665ddb94bd49767f2709b291d8a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T09:31:18Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-43e06665ddb94bd49767f2709b291d8a2023-05-25T23:00:47ZengIEEEIEEE Access2169-35362023-01-0111489014891110.1109/ACCESS.2023.327631910124725WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge GraphsKong Wei Kun0https://orcid.org/0000-0002-2958-9969Xin Liu1https://orcid.org/0000-0002-2336-7409Teeradaj Racharak2https://orcid.org/0000-0002-8823-2361Guanqun Sun3https://orcid.org/0009-0008-4704-7072Jianan Chen4Qiang Ma5https://orcid.org/0000-0003-3430-9244Le-Minh Nguyen6https://orcid.org/0000-0002-2265-1010School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, JapanArtificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, JapanSchool of Information Science, Japan Advanced Institute of Science and Technology, Nomi, JapanSchool of Information Science, Japan Advanced Institute of Science and Technology, Nomi, JapanSchool of Information Science, Japan Advanced Institute of Science and Technology, Nomi, JapanDepartment of Social Informatics, Kyoto University, Kyoto, JapanSchool of Information Science, Japan Advanced Institute of Science and Technology, Nomi, JapanWith the further development of knowledge graphs, many weighted knowledge graphs (WKGs) have been published and greatly promote various applications. However, current deterministic knowledge graph embedding algorithms cannot encode weighted knowledge graphs well. This paper gives a promising framework WeExt that can extend deterministic knowledge graph embedding models to enable them to learn weighted knowledge graph embeddings. In addtion, we introduce weighted link prediction to evaluate the weighted knowledge graph embedding models’ performance on completing WKGs. Finally, we give concrete implementation of WeExt based on two translational distance models and two semantic matching models. Our experimental results show the proposed framework achieves promising performance in link prediction, weight prediction, and weighted link prediction.https://ieeexplore.ieee.org/document/10124725/Weighted knowledge graph embeddingweighted link prediction |
| spellingShingle | Kong Wei Kun Xin Liu Teeradaj Racharak Guanqun Sun Jianan Chen Qiang Ma Le-Minh Nguyen WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs IEEE Access Weighted knowledge graph embedding weighted link prediction |
| title | WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs |
| title_full | WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs |
| title_fullStr | WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs |
| title_full_unstemmed | WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs |
| title_short | WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs |
| title_sort | weext a framework of extending deterministic knowledge graph embedding models for embedding weighted knowledge graphs |
| topic | Weighted knowledge graph embedding weighted link prediction |
| url | https://ieeexplore.ieee.org/document/10124725/ |
| work_keys_str_mv | AT kongweikun weextaframeworkofextendingdeterministicknowledgegraphembeddingmodelsforembeddingweightedknowledgegraphs AT xinliu weextaframeworkofextendingdeterministicknowledgegraphembeddingmodelsforembeddingweightedknowledgegraphs AT teeradajracharak weextaframeworkofextendingdeterministicknowledgegraphembeddingmodelsforembeddingweightedknowledgegraphs AT guanqunsun weextaframeworkofextendingdeterministicknowledgegraphembeddingmodelsforembeddingweightedknowledgegraphs AT jiananchen weextaframeworkofextendingdeterministicknowledgegraphembeddingmodelsforembeddingweightedknowledgegraphs AT qiangma weextaframeworkofextendingdeterministicknowledgegraphembeddingmodelsforembeddingweightedknowledgegraphs AT leminhnguyen weextaframeworkofextendingdeterministicknowledgegraphembeddingmodelsforembeddingweightedknowledgegraphs |