Domain-Agnostic Knowledge Graph Construction for Systematic Hallucination Reduction and Knowledge Reusability in Large Language Models

Large Language Models (LLMs) have rapidly advanced the capabilities of automated reasoning and text generation, yet they continue to hallucinate when responding to domain-specific or rapidly evolving queries due to limitations in their static, parametric knowledge. This challenge is especially significant in high-stakes domains where factual accuracy is critical. To address this gap, the present study introduces a domain-agnostic framework called the Web-Constructed Knowledge Graph (WCKG), designed to ground LLM outputs in verifiable, web-retrieved information. Unlike conventional Retrieval-Augmented Generation (RAG) pipelines, WCKG transforms ad-hoc retrieval into structured, reusable knowledge through automated, query-triggered web searches that extract entities and relations and synthesize them into lightweight, provenance-aware knowledge graphs maintained locally within user sessions. A global registry stores only abstracted metadata, ensuring decentralized knowledge management and privacy while enabling efficient indexing and discovery. Web-grounded reasoning is achieved by serializing relevant graph fragments directly into LLM prompts. Experimental evaluation demonstrates that this framework generates coherent knowledge graphs, supports iterative refinement through user interactions, and improves the reliability of model responses across diverse domains, achieving an average hallucination reduction of 3.3% over a RAG baseline. The findings imply that WCKG can convert transient LLM interactions into evolving knowledge resources, offering a practical foundation for long-term reasoning, model adaptation, and decentralized knowledge sharing in future AI systems.