ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion
Link prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge graph. Previous research on link predication only...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/11/9/1096 |
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| author | Jiangtao Ma Yaqiong Qiao Guangwu Hu Yanjun Wang Chaoqin Zhang Yongzhong Huang Arun Kumar Sangaiah Huaiguang Wu Hongpo Zhang Kai Ren |
| author_facet | Jiangtao Ma Yaqiong Qiao Guangwu Hu Yanjun Wang Chaoqin Zhang Yongzhong Huang Arun Kumar Sangaiah Huaiguang Wu Hongpo Zhang Kai Ren |
| author_sort | Jiangtao Ma |
| collection | DOAJ |
| description | Link prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge graph. Previous research on link predication only focuses on path or semantic-based features, which can hardly have a full insight of features between entities and may result in a certain ratio of false inference results. To improve the accuracy of link predication, we propose a novel approach named Entity Link Prediction for Knowledge Graph (ELPKG), which can achieve a high accuracy on large-scale knowledge graphs while keeping desirable efficiency. ELPKG first combines path and semantic-based features together to represent the relationships between entities. Then it adopts a probabilistic soft logic-based reasoning method that effectively solves the problem of non-deterministic knowledge reasoning. Finally, the relation between entities is completed based on the entity link prediction algorithm. Extensive experiments on real dataset show that ELPKG outperforms baseline methods on hits@1, hits@10, and MRR. |
| first_indexed | 2024-04-11T22:51:24Z |
| format | Article |
| id | doaj.art-07099c9a0ae44ea1a6f0257a707e04cd |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T22:51:24Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-07099c9a0ae44ea1a6f0257a707e04cd2022-12-22T03:58:34ZengMDPI AGSymmetry2073-89942019-09-01119109610.3390/sym11091096sym11091096ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph CompletionJiangtao Ma0Yaqiong Qiao1Guangwu Hu2Yanjun Wang3Chaoqin Zhang4Yongzhong Huang5Arun Kumar Sangaiah6Huaiguang Wu7Hongpo Zhang8Kai Ren9School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaState Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, ChinaSchool of Computer Science, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Computer Science and Engineering, VIT University, Vellore 632014, IndiaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaState Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, ChinaCooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, ChinaLink prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge graph. Previous research on link predication only focuses on path or semantic-based features, which can hardly have a full insight of features between entities and may result in a certain ratio of false inference results. To improve the accuracy of link predication, we propose a novel approach named Entity Link Prediction for Knowledge Graph (ELPKG), which can achieve a high accuracy on large-scale knowledge graphs while keeping desirable efficiency. ELPKG first combines path and semantic-based features together to represent the relationships between entities. Then it adopts a probabilistic soft logic-based reasoning method that effectively solves the problem of non-deterministic knowledge reasoning. Finally, the relation between entities is completed based on the entity link prediction algorithm. Extensive experiments on real dataset show that ELPKG outperforms baseline methods on hits@1, hits@10, and MRR.https://www.mdpi.com/2073-8994/11/9/1096relation completionknowledge graph completionlink predictionprobabilistic soft logic |
| spellingShingle | Jiangtao Ma Yaqiong Qiao Guangwu Hu Yanjun Wang Chaoqin Zhang Yongzhong Huang Arun Kumar Sangaiah Huaiguang Wu Hongpo Zhang Kai Ren ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion Symmetry relation completion knowledge graph completion link prediction probabilistic soft logic |
| title | ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion |
| title_full | ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion |
| title_fullStr | ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion |
| title_full_unstemmed | ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion |
| title_short | ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion |
| title_sort | elpkg a high accuracy link prediction approach for knowledge graph completion |
| topic | relation completion knowledge graph completion link prediction probabilistic soft logic |
| url | https://www.mdpi.com/2073-8994/11/9/1096 |
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