TCKGE: Transformers with contrastive learning for knowledge graph embedding
Abstract Representation learning of knowledge graphs has emerged as a powerful technique for various downstream tasks. In recent years, numerous research efforts have been made for knowledge graphs embedding. However, previous approaches usually have difficulty dealing with complex multi-relational knowledge graphs due to their shallow network architecture. In this paper, we propose a novel framework named Transformers with Contrastive learning for Knowledge Graph Embedding (TCKGE), which aims to learn complex semantics in multi-relational knowledge graphs with deep architectures. To effectively capture the rich semantics of knowledge graphs, our framework leverages the powerful Transformers to build a deep hierarchical architecture to dynamically learn the embeddings of entities and relations. To obtain more robust knowledge embeddings with our deep architecture, we design a contrastive learning scheme to facilitate optimization by exploring the effectiveness of several different data augmentation strategies. The experimental results on two benchmark datasets show the superior of TCKGE over state-of-the-art models..
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Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
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Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
International journal of multimedia information retrieval - 11(2022), 4 vom: 27. Nov., Seite 589-597 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Xiaowei [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s13735-022-00256-3 |
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OLC2080172492 |
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| 520 | |a Abstract Representation learning of knowledge graphs has emerged as a powerful technique for various downstream tasks. In recent years, numerous research efforts have been made for knowledge graphs embedding. However, previous approaches usually have difficulty dealing with complex multi-relational knowledge graphs due to their shallow network architecture. In this paper, we propose a novel framework named Transformers with Contrastive learning for Knowledge Graph Embedding (TCKGE), which aims to learn complex semantics in multi-relational knowledge graphs with deep architectures. To effectively capture the rich semantics of knowledge graphs, our framework leverages the powerful Transformers to build a deep hierarchical architecture to dynamically learn the embeddings of entities and relations. To obtain more robust knowledge embeddings with our deep architecture, we design a contrastive learning scheme to facilitate optimization by exploring the effectiveness of several different data augmentation strategies. The experimental results on two benchmark datasets show the superior of TCKGE over state-of-the-art models. | ||
| 650 | 4 | |a Augmentation | |
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| 700 | 1 | |a Fang, Quan |0 (orcid)0000-0003-4190-1529 |4 aut | |
| 700 | 1 | |a Hu, Jun |4 aut | |
| 700 | 1 | |a Qian, Shengsheng |4 aut | |
| 700 | 1 | |a Xu, Changsheng |4 aut | |
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