Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Knowledge graphs (KGs) are structured representations of entities and their relationships, widely used in domains like natural language processing, recommendation systems, and semantic search. Despite their utility, knowledge graphs are often incomplete, containing missing entities or relations, which can reduce their effectiveness. Knowledge graph completion (KGC) aims to infer these missing links and enhance the graph’s coverage. Recently, deep learning approaches have shown significant improvements in KGC by effectively modeling complex patterns and semantic information within large-scale graphs. This paper provides a comprehensive review of KGC methods leveraging deep learning, including embedding-based models, graph neural networks (GNNs), and hybrid architectures. Comparative analyses, challenges, and future research directions are discussed. The paper also presents key datasets, evaluation metrics, and illustrative results through tables and figures to highlight trends and performance in KGC research.

Keywords: Knowledge Graph Completion, Deep Learning, Graph Neural Networks, Embedding Models, Link Prediction, Semantic Knowledge Graphs