Knowledge Graph Completion using Structural and Textual Embeddings

TL;DR

This paper introduces a relation prediction model for knowledge graph completion that combines textual and structural embeddings, improving the prediction of missing relations by integrating walk-based and language model embeddings.

Contribution

The study presents a novel approach that effectively combines structural and textual information for relation prediction in knowledge graphs, enhancing completion accuracy.

Findings

Achieves competitive results on standard datasets.

Effectively integrates walk-based and language model embeddings.

Improves relation prediction accuracy in knowledge graphs.

Abstract

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.