🤖 AI Summary
Traditional semantic search struggles to model the hierarchical structures and multi-hop cross-references prevalent in enterprise documents, limiting retrieval accuracy. This work proposes an agent-driven, recursive knowledge graph construction approach that automatically parses substitutional logic and cross-level references among documents to generate a structured graph representation. The resulting knowledge graph is integrated into a retrieval-augmented generation (RAG) framework to enable precise querying of complex regulatory logic. Evaluated on the Code of Federal Regulations benchmark, the proposed method achieves a 70% improvement in question-answering accuracy over standard vector-based RAG systems, substantially overcoming the limitations of conventional semantic retrieval.
📝 Abstract
This research paper addresses the limitations of semantic search in complex enterprise document ecosystems. Traditional RAG pipelines often fail to capture hierarchical and interconnected information, leading to retrieval inaccuracies. We propose Agentic Knowledge Graphs featuring Recursive Crawling as a robust solution for navigating superseding logic and multi-hop references. Our benchmark evaluation using the Code of Federal Regulations (CFR) demonstrates that this Knowledge Graph-enhanced approach achieves a 70% accuracy improvement over standard vector-based RAG systems, providing exhaustive and precise answers for complex regulatory queries.