Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs

TL;DR

This paper introduces recurrent skipping networks (RSNs) that leverage residual and recurrent neural network mechanisms to effectively capture long-term relational dependencies in knowledge graphs, improving entity alignment and KG completion.

Contribution

The paper proposes RSNs, a novel neural network architecture that captures long-term dependencies in KGs, addressing limitations of existing triple-level embedding methods.

Findings

RSNs outperform state-of-the-art methods in entity alignment.

RSNs achieve competitive results in KG completion tasks.

The approach effectively propagates semantic information across entities.

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.