TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs
Abstrak
Learning knowledge graph (KG) embeddings has received increasing attention in recent years. Most embedding models in literature interpret relations as linear or bilinear mapping functions to operate on entity embeddings. However, we find that such relation-level modeling cannot capture the diverse relational structures of KGs well. In this paper, we propose a novel edge-centric embedding model TransEdge, which contextualizes relation representations in terms of specific head-tail entity pairs. We refer to such contextualized representations of a relation as edge embeddings and interpret them as translations between entity embeddings. TransEdge achieves promising performance on different prediction tasks. Our experiments on benchmark datasets indicate that it obtains the state-of-the-art results on embedding-based entity alignment. We also show that TransEdge is complementary with conventional entity alignment methods. Moreover, it shows very competitive performance on link prediction.
Topik & Kata Kunci
Penulis (6)
Zequn Sun
Jiacheng Huang
Wei Hu
Muhao Chen
Lingbing Guo
Yuzhong Qu
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2019
- Bahasa
- en
- Total Sitasi
- 152×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1007/978-3-030-30793-6_35
- Akses
- Open Access ✓