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A Quaternion-Embedded Capsule Network Model for Knowledge Graph Completion
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DOI：10.1109/ACCESS.2020.2997177.png)
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摘要
En 中文
Knowledge graphs are collections of factual triples. Link prediction aims to predict lost factual triples in knowledge graphs. In this paper, we present a novel capsule network method for link prediction taking advantages of quaternion. More specifically, we explore two methods, including a relational rotation model called QuaR and a deep capsule neural model called CapS-QuaR to encode semantics of factual triples. QuaR model defines each relation as a rotation from the head entity to the tail entity in the hyper-complex vector space, which could be used to infer and model diverse relation patterns, including: symmetry/anti-symmetry, reversal and combination. Based on these characteristics of quaternions, we use the embeddings of entities and relations trained from QuaR as the input to CapS-QuaR model. Experimental results on multiple benchmark knowledge graphs show that the proposed method is not only scalable, but also able to predict the correctness of triples in knowledge graphs and significantly outperform the existing state-of-the-art models for link prediction. Finally, the evaluation of a real dataset for search personalization task is conducted to prove the effectiveness of our model.
Keyword:
Quaternions
Knowledge engineering
Task analysis
Convolutional neural networks
Predictive models
Solid modeling
Capsule network
knowledge graph
knowledge graph completion
link prediction
quaternion
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Kinematic analysis of limb movements in neuropsychological research: Subtle deficits and recovery of function.神经心理学研究中肢体运动的运动学分析: 细微的缺陷和功能的恢复。
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分享收藏DBpedia - A large-scale, multilingual knowledge base extracted from WikipediaDBpedia-从维基百科中提取的大规模多语言知识库.png)
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