JGURD: joint gradient update relational direction-enhanced method for knowledge graph completion
Lianhong Ding, Mengxiao Li, Shengchang Gao, Juntao Li, Ruiping Yuan, Jianye Yu
TL;DR
JGURD is a new method for completing knowledge graphs that better uses relationship directions and improves performance over existing techniques.
Contribution
JGURD introduces a joint gradient update mechanism with relational direction information and a relation correlation graph for enhanced knowledge graph completion.
Findings
JGURD outperforms HHAN-KGC in knowledge graph completion tasks.
Hits@3 and MRR metrics improved by 6.8% and 8.9% on the FB15k dataset.
A relation correlation graph captures local and global structures effectively.
Abstract
Relational direction plays an important role in multi-relational knowledge graphs (KGs). Current knowledge graph completion (KGC) methods suffer from insufficient utilization of relation correlation information. To address this issue, this article proposes a novel KGC framework, namely JGURD, which uses the encoder-decoder structure to achieve Joint Gradient Update with Relational Direction information. It combines graph convolutional networks (GCNs) with KG embedding methods, defining a update mechanism for entities and relationships to joint gradient updates. To incorporate entity information into the update of relationships, the forward propagation gradients of the triple score function are recorded, and entity gradient information is fused into relationship updates. To fully utilize relational direction information, a relation correlation graph (RCG) is constructed based on the…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Multimodal Machine Learning Applications · Text and Document Classification Technologies