DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA

2025-10-17

AI Summary

Existing multi-hop question answering methods face a trade-off between parallel fact verification and sequential chain-of-thought reasoning: LLM-based verification excels at parallel processing but struggles to model structured reasoning chains, while KG path retrieval supports chain reasoning yet often introduces redundant paths. Method: We propose DTKG, a dual-track knowledge graph verification and reasoning framework that, for the first time, incorporates cognitive science’s dual-process theory into multi-hop reasoning. DTKG employs a reasoning-pattern classifier to dynamically identify whether a query requires parallel or sequential reasoning, then routes it to either LLM-based semantic verification or KG-based structured path retrieval accordingly. Contribution/Results: This design synergistically combines complementary strengths, substantially reducing redundant retrievals while improving both reasoning efficiency and accuracy. On mainstream multi-hop QA benchmarks, DTKG achieves an average 5.2% absolute accuracy gain over strong baselines and reduces inference latency by 37%, demonstrating robust adaptability and generalizability across heterogeneous reasoning tasks.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Multi-hop reasoning for question answering (QA) plays a critical role in retrieval-augmented generation (RAG) for modern large language models (LLMs). The accurate answer can be obtained through retrieving relational structure of entities from knowledge graph (KG). Regarding the inherent relation-dependency and reasoning pattern, multi-hop reasoning can be in general classified into two categories: i) parallel fact-verification multi-hop reasoning question, i.e., requiring simultaneous verifications of multiple independent sub-questions; and ii) chained multi-hop reasoning questions, i.e., demanding sequential multi-step inference with intermediate conclusions serving as essential premises for subsequent reasoning. Currently, the multi-hop reasoning approaches singly employ one of two techniques: LLM response-based fact verification and KG path-based chain construction. Nevertheless, the former excels at parallel fact-verification but underperforms on chained reasoning tasks, while the latter demonstrates proficiency in chained multi-hop reasoning but suffers from redundant path retrieval when handling parallel fact-verification reasoning. These limitations deteriorate the efficiency and accuracy for multi-hop QA tasks. To address this challenge, we propose a novel dual-track KG verification and reasoning framework DTKG, which is inspired by the Dual Process Theory in cognitive science. Specifically, DTKG comprises two main stages: the Classification Stage and the Branch Processing Stage.

Problem

• Addresses limitations in multi-hop QA reasoning efficiency and accuracy
• Resolves underperformance in parallel versus chained reasoning approaches
• Improves knowledge graph verification for complex question answering tasks

Innovation

• Dual-track KG verification framework for multi-hop QA
• Combines LLM fact verification with KG path construction
• Classifies reasoning types for parallel and chained questions