An Intelligent Framework for Knowledge Graph Construction using Hierarchical Context-Aware Semantic Representations

Abstract

The rapid growth of unstructured textual data necessitates automated approaches for transforming such information into structured, machine-readable knowledge. Knowledge Graphs (KGs) provide an effective framework for representing entities and their relationships; however, existing methods often suffer from fragmented pipelines, limited semantic consistency, and challenges in handling domain-specific variations. This paper presents an intelligent and scalable approach for knowledge graph construction from semantically enriched keyword-based inputs derived from a context-aware extraction process. The proposed method employs a unified pipeline comprising entity identification and ontology-based linking, context-aware relation extraction, and structured triple generation in the form of subject-predicate-object (SPO) representations. The generated triples are futher transformed into RDF format and organized into a coherent knowledge graph, followed by refinement steps to ensure semantic consistency and structural integrity. The approach is evaluated on representative datasets, including PubMed abstracts, and demonstrates improved performance in triplet extraction, entity and relation accuracy, and graph-level quality metrics such as density, clustering coefficient, and modularity. Comparative analysis with baseline methods highlights the effectiveness of the proposed approach in generating coherent and semantically enriched knowledge graphs. Additionally, the system exhibits strong scalability and computational efficiency, making it suitable for large-scale and real-world applications acreoss diverse domains. Overall, the proposed approach effectively bridges the gap between unstructured text and structured knowledge representation, enabling reliable, scalable, and high-quality knowledge graph construction.