Authors - Lalitha R, Husna Sarirah Husin, Suriana Ismail, Nikitha S, Kavya Darshini S, Pooja M Abstract - The data from Tamil Nadu government MSME programs is a treasure trove, but the information is fragmented and scattered in different kinds of documents. Consequently, it becomes a task for both the public and the analysts to process the data and get important insights. The paper introduces LKD-RAG, an explainable hybrid retrieval-augmented generation (RAG) system that relies on LLMs and KGs to make natural language queries possible on the data of these schemes collected from different sources. In the initial phase, the LLM started autonomously to discover entities, relations, and attributes, which eventually led to the creation of structured triples that signify factual statements (subject-predicate-object). The knowledge represented by these triples was loaded into Neo4j, thereby producing a MSME Scheme KG that is specific to the domain. Also, a document embedding layer was set up with SentenceTransformer ("all-MiniLM-L6-v2") that made it possible to do semantic retrieval of supporting textual evidence. When a query is made, Gemini decodes the person’s inquiry, finds relevant KG subgraphs and text embeddings, and constructs a response that is grounded on the evidence. The subgraph that corresponds to the answer is shown to the user, so the user can check what knowledge the model is relying on for its reasoning. Thus, the process facilitates transparency and the use of explainable AI (XAI) in policy analytics. The results of the experiments indicate that the hybrid RAG model not only has the ability to generate factually accurate responses but also to provide interpretation through different Tamil Nadu MSME programs.
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