Knowledge Graph Completion using Structural and Textual Embeddings
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Abstract (arXiv)
Knowledge Graphs (KGs) are widely employed in artificial intelligence applications, such as question-answering and recommendation systems. However, KGs are frequently found to be incomplete. While much of the existing literature focuses on predicting missing nodes for given incomplete KG triples, there remains an opportunity to complete KGs by exploring relations between existing nodes, a task known as relation prediction. In this study, we propose a relations prediction model that harnesses both textual and structural information within KGs. Our approach integrates walks-based embeddings with language model embeddings to effectively represent nodes. We demonstrate that our model achieves competitive results in the relation prediction task when evaluated on a widely used dataset.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 38 references that are in the map. First posted on arXiv 24 Apr 2024.
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22 of 38 references.
References · 22
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- 2020Inductive Entity Representations from Text via Link Predictioncited by 62
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- 2021A shallow neural model for relation predictioncited by 6
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