<p>Hindu scriptures, particularly the Vedas, encompass profound philosophical and spiritual knowledge conveyed through symbolic language and complex textual structures. However, their unstructured form presents significant challenges for computational analysis, semantic retrieval, and digital preservation. Although knowledge graphs have demonstrated success in representing structured information across diverse domains, they often fall short in capturing the intricate semantics embedded in scriptural literature. This paper introduces a structured model for constructing a knowledge graph for the Hindu scriptural context. The methodology comprises four phases: textual data collection, extraction of concepts and relationships, graph construction, and visual representation. To demonstrate the applicability of the proposed approach, selected portions of the Vedas are used as a representative case. The resulting graph, developed using the Neo4j database and the Cypher query language, comprises 50 distinct entities and 109 semantically enriched relationships. Unlike domain-general knowledge graphs, this model emphasises hierarchical and contextual relationships relevant to spiritual texts. The study offers a foundational framework for future applications in semantic search, educational platforms, and natural language-based Question answering systems that aim to enhance accessibility and understanding of traditional Hindu knowledge systems.</p>