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| Dynamic Knowledge Graph Inference Based on Multiple Relational Cyclic Events |
| CHEN Hao1, LI Yongqiang1, FENG Yuanjing1 |
| 1.College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023 |
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Abstract The reasoning ability of most existing dynamic knowledge map reasoning methods under the same time and multiple relationships is limited . Aiming at this problem, a method of dynamic knowledge graph inference based on multi-relational cyclic events(Multi-Net) is proposed. The improved multi-relational proximity aggregator is employed to fuse target entity neighborhood information to obtain more accurate representation of entity neighborhood vector, and Multi-Net is simplified by optimizing information fusion, and the ability to handle the conflict of relations between two entities in a specific scope is improved by adding the relationship prediction task to Multi-Net. Experiments of entity prediction and relationship prediction on large real datasets indicate that Multi-Net improves the reasoning ability of dynamic knowledge maps effectively.
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Received: 31 December 2019
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Corresponding Authors:
LI Yongqiang, Ph.D., lecturer. His research interests include data-driven control, optimal control and natural language processing.
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| About author:: CHEN Hao, master student. His research interests include knowledge representation learning and knowledge graph reasoning..FENG Yuanjing, Ph.D., professor. His research interests include image processing, intelligent optimization and natural language processing. |
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2020, Vol. 33 
