| 摘 要: 在知识图谱(Knowledge Graph)中，知识表示方法旨在通过一种低维稠密的向量表示方法来高效地挖掘
不同实体、关系之间复杂语义关系，在知识问答、信息检索等领域有着重要意义。然而，现有的绝大多数的知识表示方
法忽略了时间因素，无法表示应用中随时间变化的动态知识。针对该问题，本文提出一种基于实体时间敏感度的知识表
示方法。该方法将时间信息以不同程度融入不同类型的实体向量表示中，然后进行实体和关系之间语义挖掘。实验结果
表明，这种基于实体时间敏感度的表示方法能够明显提高知识图谱的时态知识补全和预测任务性能。 |
| 关键词: 知识图谱；表示学习；时态知识；复杂关系；知识补全 |
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中图分类号: TP391.1
文献标识码: A
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| 基金项目: 国家重点研发计划课题(2017YFB1201001). |
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| A Knowledge Representation Method Based on Entity Time Sensitivity |
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TIAN Manxin,SHOU Lidan,CHEN Ke,JIANG Dawei,CHEN Gang1,2
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1.( 1.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;2.
2.Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, Hangzhou 310027, China)
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| Abstract: Representation learning in knowledge graph aims to explore the complex semantic relationship between entities and their relations with a low-dimensional,dense vectors representation method,which is of great significance in the fields of knowledge question and answer and information retrieval.However,most of the existing knowledge representation methods ignore the time factor and cannot express the dynamic knowledge of the application over time.For this problem,this paper proposes a knowledge representation method based on entity time sensitivity.This method integrates the time information into different types of the entity vector representation with different degrees,and then performs semantic mining between entities and their relationships.Experimental results show that this entity time sensitivity based representation method can obviously improve the temporal knowledge completion and prediction task performance of the knowledge graph. |
| Keywords: knowledge graph;representation learning;temporal knowledge;complex relationship;knowledge complement |
