Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs

Lingbing Guo; Zequn Sun; Wei Hu

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arXiv · 1905.04914

Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs

Lingbing Guo·Zequn Sun·Wei Hu

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Abstract

We study the problem of knowledge graph (KG) embedding. A widely-established assumption to this problem is that similar entities are likely to have similar relational roles. However, existing related methods derive KG embeddings mainly based on triple-level learning, which lack the capability of capturing long-term relational dependencies of entities. Moreover, triple-level learning is insufficient for the propagation of semantic information among entities, especially for the case of cross-KG embedding. In this paper, we propose recurrent skipping networks (RSNs), which employ a skipping mechanism to bridge the gaps between entities. RSNs integrate recurrent neural networks (RNNs) with residual learning to efficiently capture the long-term relational dependencies within and between KGs. We design an end-to-end framework to support RSNs on different tasks. Our experimental results showed that RSNs outperformed state-of-the-art embedding-based methods for entity alignment and achieved competitive performance for KG completion.

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Lingbing Guo, Zequn Sun, Wei Hu. 2019-05-13. Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs. https://arxiv.org/abs/1905.04914

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