Positionally restricted masked knowledge graph completion via multi-head mutual attention
Knowledge graph completion aims to enhance the completeness of knowledge graphs by predicting missing links. Link prediction is a common approach for this task, but existing methods, particularly those based on similarity computation, are often computationally expensive, especially for large models....
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-05-01
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| Series: | Journal of Information and Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949715925000095 |
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| _version_ | 1849472851210403840 |
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| author | Qiang Yu Liang Bao Peng Nie Lei Zuo |
| author_facet | Qiang Yu Liang Bao Peng Nie Lei Zuo |
| author_sort | Qiang Yu |
| collection | DOAJ |
| description | Knowledge graph completion aims to enhance the completeness of knowledge graphs by predicting missing links. Link prediction is a common approach for this task, but existing methods, particularly those based on similarity computation, are often computationally expensive, especially for large models. To address this, we propose a novel method, positionally restricted masked knowledge graph completion (PR-MKGC), which reduces inference time by leveraging masked prediction and relying solely on structural information from the knowledge graph, without using textual data. We introduce a multi-head mutual attention mechanism that aggregates neighbor information more effectively, improving the model's ability to predict missing links. Experimental results demonstrate that PR-MKGC outperforms existing models in terms of both predictive performance and inference time on the FB15K-237 dataset. |
| format | Article |
| id | doaj-art-bb6221b07c3d474c8f7d5f0786bad17d |
| institution | Kabale University |
| issn | 2949-7159 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Information and Intelligence |
| spelling | doaj-art-bb6221b07c3d474c8f7d5f0786bad17d2025-08-20T03:24:22ZengKeAi Communications Co., Ltd.Journal of Information and Intelligence2949-71592025-05-013321022210.1016/j.jiixd.2025.02.006Positionally restricted masked knowledge graph completion via multi-head mutual attentionQiang Yu0Liang Bao1Peng Nie2Lei Zuo3Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China; School of Computer Science and Technology, Xidian University, Xi'an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi'an 710071, ChinaHangzhou Institute of Technology, Xidian University, Hangzhou 311231, China; Corresponding author.National Lab of Radar Signal Processing, Xidian University, Xi'an 710071, China; Corresponding author.Knowledge graph completion aims to enhance the completeness of knowledge graphs by predicting missing links. Link prediction is a common approach for this task, but existing methods, particularly those based on similarity computation, are often computationally expensive, especially for large models. To address this, we propose a novel method, positionally restricted masked knowledge graph completion (PR-MKGC), which reduces inference time by leveraging masked prediction and relying solely on structural information from the knowledge graph, without using textual data. We introduce a multi-head mutual attention mechanism that aggregates neighbor information more effectively, improving the model's ability to predict missing links. Experimental results demonstrate that PR-MKGC outperforms existing models in terms of both predictive performance and inference time on the FB15K-237 dataset.http://www.sciencedirect.com/science/article/pii/S2949715925000095Knowledge graphLink predictionAttention mechanismInformation aggregation |
| spellingShingle | Qiang Yu Liang Bao Peng Nie Lei Zuo Positionally restricted masked knowledge graph completion via multi-head mutual attention Journal of Information and Intelligence Knowledge graph Link prediction Attention mechanism Information aggregation |
| title | Positionally restricted masked knowledge graph completion via multi-head mutual attention |
| title_full | Positionally restricted masked knowledge graph completion via multi-head mutual attention |
| title_fullStr | Positionally restricted masked knowledge graph completion via multi-head mutual attention |
| title_full_unstemmed | Positionally restricted masked knowledge graph completion via multi-head mutual attention |
| title_short | Positionally restricted masked knowledge graph completion via multi-head mutual attention |
| title_sort | positionally restricted masked knowledge graph completion via multi head mutual attention |
| topic | Knowledge graph Link prediction Attention mechanism Information aggregation |
| url | http://www.sciencedirect.com/science/article/pii/S2949715925000095 |
| work_keys_str_mv | AT qiangyu positionallyrestrictedmaskedknowledgegraphcompletionviamultiheadmutualattention AT liangbao positionallyrestrictedmaskedknowledgegraphcompletionviamultiheadmutualattention AT pengnie positionallyrestrictedmaskedknowledgegraphcompletionviamultiheadmutualattention AT leizuo positionallyrestrictedmaskedknowledgegraphcompletionviamultiheadmutualattention |