Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Entity-aware ranking represents a transformative evolution in information retrieval by integrating knowledge graph structures into search algorithms. This integration bridges the gap between traditional keyword matching and semantic understanding, enabling systems to comprehend the contextual relationships between entities present in both queries and content. Knowledge graphs provide a structured framework for representing real-world entities and their interconnections, allowing search systems to reason about concepts rather than merely matching text patterns. The implementation of entity-aware ranking has demonstrated significant improvements across various domains, including web search, e-commerce, scholarly literature, and enterprise knowledge management. Entity linking serves as the foundation of these systems, converting unstructured text into semantically structured representations with high precision and recall rates. Graph-based relevance computation leverages network structures to discover meaningful connections between entities, while hybrid ranking models synthesize multiple information signals through sophisticated machine learning frameworks. Despite technical challenges in knowledge graph construction, computational efficiency, and ambiguity management, advanced solutions have emerged through embedding techniques, efficient algorithms, and probabilistic reasoning frameworks. The fusion of knowledge graphs with neural ranking methods has proven particularly effective in balancing statistical patterns with structured knowledge, transforming information retrieval from isolated query processing to genuine understanding of user intent.