Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs

By Lingbing Guo, Zequn Sun, Wei Hu. International Conference on Machine Learning, 2019

308 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/85cf5bb1a669ed09b77af27153ad5358f52e5886

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In Artificial Intelligence

1. Philip S. Yu
2. Shirui Pan
3. Wei Hu
4. Zequn Sun
5. Erik Cambria
6. Shaoxiong Ji
7. Muhao Chen
8. Chengming Wang

Citations in the corpus

• A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, unknown): “RSN4EA [25] Path - Cosine Sharing Superv.”
• Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction (2019, methodology): “To train the model, we use the negative sampling loss functions with self-adversarial training (Sun et al. 2019): where γ is a fixed margin, σ is the sigmoid function, and ( h i , r, t i ) is the i th negative triple.”
• Knowledge Graph Embedding for Link Prediction (2020, methodology): “In FB15K-237, all models display globally worse performance; nonetheless, interestingly most of them manage to keep good performance on reƒexive relations, the exceptions being ANALOGY , SimplE [27], ConvE [11] and RSN [19].”
• Neural, symbolic and neural-symbolic reasoning on knowledge graphs (2021, methodology): “P Q (Bordes et al., 2013) force h þ r to be close to t TransH (Wang et al., 2014) project entities into the relation-speci fi c hyperplane TransR (Lin et al., 2015) project entities and relations in separate spaces TransD (Ji et al., 2015) determine the project matrices by both entities and relations”
• A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “RSN [45] (Fig.”
• Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation (2019, methodology): “We compare with recent embedding-based entity alignment models: MTransE (Chen et al. 2017), IPTransE (Zhu et al. 2017), JAPE (Sun, Hu, and Li 2017), AlignE (Sun et al. 2018), GCN-Align (Wang et al. 2018), SEA (Pei et al. 2019), RSN (Guo, Sun, and Hu 2019) and MuGNN (Cao et al. 2019).”