Generative Artificial Intelligence-Driven Enterprise Knowledge Base

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As enterprises generate vast amounts of heterogeneous knowledge, existing knowledge management systems face challenges in providing contextually relevant, accurate, and domain-specific answers. To address these issues, the authors propose KQGen, a unified framework that integrates knowledge graph embeddings with generative question answering for enterprise knowledge bases. KQGen encodes knowledge graph entities and relations, performs query understanding, and generates answers grounded in structured knowledge, optimized by a reinforcement learning-based feedback mechanism. Experimental results on benchmarks such as SQuAD, TREC, and KBQA show that KQGen outperforms existing models in answer relevance and ranking metrics. However, challenges remain in improving performance for Exact Match (EM) and scalability. Future work will focus on refining multi-hop reasoning, real-time scalability, and integrating multi-modal data for enhanced system adaptability.