Real World Knowledge Graph

Real-world knowledge graphs (KGs) are incomplete and noisy representations of factual information, hindering their utility for various applications. Current research focuses on improving knowledge graph completion (KGC) techniques, employing methods like graph neural networks, language models, and tensor decomposition-based models to predict missing links and reason over complex relationships. These advancements address challenges such as data sparsity, the handling of temporal dynamics, and the incorporation of diverse information sources, including entity types and neighborhood context, to enhance accuracy and robustness. Improved KGC methods have significant implications for various fields, enabling more accurate and efficient knowledge discovery, reasoning, and information retrieval.