Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions

• URL: http://arxiv.org/abs/2009.12765v1
• Date: Sun, 27 Sep 2020 07:12:18 GMT
• Title: Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions
• Authors: Damai Dai, Hua Zheng, Fuli Luo, Pengcheng Yang, Baobao Chang, Zhifang Sui
• Abstract summary: We propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Experimental results show that our method outperforms the state-of-the-art methods with higher efficiency on two KGC tasks with OOKG entities.
• Score: 42.626395991024545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to represent these entities efficiently. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method needs no additional learning. Experimental results show that our method outperforms the state-of-the-art methods with higher efficiency on two KGC tasks with OOKG entities.

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