Knowledge Graph Construction and Link Prediction Using Graph Embedding Techniques: Applications in Recommender Systems

Abstract

Recent advancements in knowledge graph construction and link prediction have significantly transformed the ways in which large-scale relational data are processed, analyzed, and utilized in complex real-world applications. By leveraging graph embedding techniques, it is possible to efficiently learn vector representations of entities and relations in a low-dimensional space, thereby enabling more accurate and scalable methods for inferring missing links and uncovering latent patterns. This approach holds particular relevance in recommender systems, where predicting potential connections among users, items, and contextual factors is critical to delivering precise and personalized suggestions. In this work, we undertake a thorough investigation of knowledge graph construction and link prediction, examining essential building blocks, structured representations, and advanced graph embedding methods that provide deep insights into complex relational data. We also discuss logical consistency requirements and the alignment of symbolic knowledge with high-dimensional numerical representations to ensure robust interpretability. Furthermore, we highlight the emerging trends and outstanding challenges in integrating graph-based recommendations, including scalability, explainability, and adaptability issues. Our analysis not only consolidates fundamental principles but also illustrates contemporary breakthroughs and open avenues for future research. Through this comprehensive exploration, the paper emphasizes how synergy between knowledge graphs and graph embedding techniques can drive next-generation recommender systems to offer unparalleled accuracy and impactful user experiences.