VEM$^2$L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion

• URL: http://arxiv.org/abs/2207.01528v1
• Date: Mon, 4 Jul 2022 15:50:21 GMT
• Title: VEM$^2$L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion
• Authors: Tao He, Tianwen Jiang, Zihao Zheng, Haichao Zhu, Jingrun Zhang, Ming Liu, Sendong Zhao and Bin Qin
• Abstract summary: We propose a plug-and-play framework VEM2L over sparse Knowledge Graphs to fuse knowledge extracted from text and structure messages into a unity. Specifically, we partition knowledge acquired by models into two nonoverlapping parts. We also propose a new fusion strategy proved by Variational EM algorithm to fuse the generalization ability of models.
• Score: 14.537509860565706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Graph Completion has been widely studied recently to complete missing elements within triples via mainly modeling graph structural features, but performs sensitive to the sparsity of graph structure. Relevant texts like entity names and descriptions, acting as another expression form for Knowledge Graphs (KGs), are expected to solve this challenge. Several methods have been proposed to utilize both structure and text messages with two encoders, but only achieved limited improvements due to the failure to balance weights between them. And reserving both structural and textual encoders during inference also suffers from heavily overwhelmed parameters. Motivated by Knowledge Distillation, we view knowledge as mappings from input to output probabilities and propose a plug-and-play framework VEM2L over sparse KGs to fuse knowledge extracted from text and structure messages into a unity. Specifically, we partition knowledge acquired by models into two nonoverlapping parts: one part is relevant to the fitting capacity upon training triples, which could be fused by motivating two encoders to learn from each other on training sets; the other reflects the generalization ability upon unobserved queries. And correspondingly, we propose a new fusion strategy proved by Variational EM algorithm to fuse the generalization ability of models, during which we also apply graph densification operations to further alleviate the sparse graph problem. By combining these two fusion methods, we propose VEM2L framework finally. Both detailed theoretical evidence, as well as quantitative and qualitative experiments, demonstrates the effectiveness and efficiency of our proposed framework.

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