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.
Lingbing Guo (Nanjing University)
Zequn Sun (Nanjing University)
Wei Hu (Nanjing University)
Related Events (a corresponding poster, oral, or spotlight)
2019 Oral: Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs »
Thu Jun 13th 06:20 -- 06:25 PM Room Hall B