Distributed Representations of Entities in Open-World Knowledge Graphs

• URL: http://arxiv.org/abs/2010.08114v2
• Date: Thu, 4 Apr 2024 03:12:50 GMT
• Title: Distributed Representations of Entities in Open-World Knowledge Graphs
• Authors: Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yichi Zhang, Zequn Sun, Zhongpo Bo, Yin Fang, Xiaoze Liu, Huajun Chen, Wen Zhang,
• Abstract summary: Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. We introduce Decentralized Attention Network (DAN) to observe only new entities. Our method significantly outperforms existing methods in open-world settings.
• Score: 47.406633469890686
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all entities during training, posing a challenge in real-world knowledge graphs where new entities emerge frequently. To address this limitation, we introduce Decentralized Attention Network (DAN). DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings. To effectively train a DAN, we introduce self-distillation, a technique that guides the network in generating desired representations. Theoretical analysis validates the effectiveness of our approach. We implement an end-to-end framework and conduct extensive experiments to evaluate our method, showcasing competitive performance on conventional entity alignment and entity prediction tasks. Furthermore, our method significantly outperforms existing methods in open-world settings.

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