Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
This work addresses the challenge of improving knowledge graph embeddings for tasks like entity alignment and completion, offering a novel method to capture long-term dependencies, though it is incremental in building on existing neural network techniques.
The paper tackled the problem of knowledge graph embedding by addressing the limitation of existing methods in capturing long-term relational dependencies, proposing recurrent skipping networks (RSNs) that outperformed state-of-the-art methods for entity alignment and achieved competitive performance for KG completion.
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.