Abstract
Knowledge Graph Complementation (KGC) aims to predict the missing triples in incomplete knowledge graphs (KGs). However, existing approaches rely either on structural features, semantic features or logical rules. There is not yet a unified way to exploit all three features mentioned above. To address this problem, this paper proposes a new KGC framework, SSL, which jointly embeds structure-augmented semantics representation in the natural language description of triples with their logical rules. First, SSL fine-tunes a pre-trained language model to capture the semantic information in the natural language description corresponding to the triple. Then, for logical rules corresponding to different relation types, we augment them to embeddings of entities and relations to activate stronger representation capabilities. Finally, by jointly training, we can obtain more expressive embeddings for the downstream KGC task. Our extensive experiments on a variety of KGC benchmarks have demonstrated the effectivity of our method.
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Jiang, J., Xu, L. (2023). Jointly Learning Structure-Augmented Semantic Representation and Logical Rules for Knowledge Graph Completion. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds) Web Information Systems and Applications. WISA 2023. Lecture Notes in Computer Science, vol 14094. Springer, Singapore. https://doi.org/10.1007/978-981-99-6222-8_5
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DOI: https://doi.org/10.1007/978-981-99-6222-8_5
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