首页|Uncertain knowledge graph embedding:an effective method combining multi-relation and multi-path

Uncertain knowledge graph embedding:an effective method combining multi-relation and multi-path

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  • NSTL
  • 万方数据
Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this problem.However,there are still unresolved issues.On the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information loss.On the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be addressed.To address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this paper.First,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is introduced.Second,the asymmetry in the UKG is defined.To embed asymmetric relation facts of UKG,a multi-relation embedding model is proposed.Finally,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE.The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.

knowledge representationuncertain knowledge graphmulti-relation embeddinguncertain reasoning

Qi LIU、Qinghua ZHANG、Fan ZHAO、Guoyin WANG

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Key Laboratory of Tourism Multisource Data Perception and Decision,Ministry of Culture and Tourism,Chongqing University of Posts and Telecommunications,Chongqing 400065,China

Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China

Chongqing Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,Chongqing 400065,China

National Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of ChongqingNatural Science Foundation of ChongqingKey Cooperation Project of Chongqing Municipal Education Commission

2020YFC20035022021YFF070410162276038cstc2019jcyjcxttX0002cstc2021ycjhbgzxm0013HZ20210-08

2024

10.1007/s11704-023-2427-z

计算机科学前沿
高等教育出版社

计算机科学前沿

CSTPCDEI
影响因子：0.303
ISSN：2095-2228
年,卷(期)：2024.18(3)
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