Knowledge Graph Completion using Structural and Textual Embeddings
This addresses the incompleteness issue in knowledge graphs, which is crucial for applications like question-answering and recommendation systems, but the approach is incremental as it builds on existing methods.
The paper tackles the problem of incomplete knowledge graphs by predicting missing relations between existing nodes, achieving competitive results on a widely used dataset.
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.