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KGMP: Augmenting retrieval knowledge graph with multi-hop perceptron.

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  • 1Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.

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|October 6, 2025
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Summary
This summary is machine-generated.

This study introduces the Knowledge Graph Multi-hop Perceptron (KGMP), a novel framework enhancing knowledge base question answering (KBQA) by improving multi-hop reasoning in knowledge graphs. KGMP significantly boosts performance on complex queries.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Knowledge Representation and Reasoning

Background:

  • Knowledge Base Question Answering (KBQA) aims to translate natural language queries into structured graph queries (GQL).
  • Existing KBQA methods struggle with the structural differences between GQL and SQL, and limited subgraph information for multi-hop reasoning.
  • Subgraph scalability limitations in current approaches hinder multi-hop query performance.

Purpose of the Study:

  • To propose the Knowledge Graph Multi-hop Perceptron (KGMP), a retrieval-generation framework for improved KBQA.
  • To address the dual challenges in KBQA: GQL/SQL structural differences and multi-hop subgraph information scarcity.
  • To enhance the performance of large language models (LLMs) in deep collaboration with knowledge graphs.

Main Methods:

  • Developed a Dynamic Graph Traversal Mechanism using iterative subgraph expansion for progressive reasoning.
  • Designed a Structured Interaction Protocol based on SparQL syntax for efficient LLM-knowledge graph communication.
  • Implemented Graph Structure Optimization Techniques, including subgraph reordering and pruning, for compact and semantically complete subgraph inputs.

Main Results:

  • Integrated KGMP as a retrieval module into the ChatKBQA framework.
  • Achieved performance improvements of 6.2% on the WebQSP dataset and 5.3% on the CWQ dataset.
  • Demonstrated enhanced multi-hop query performance through optimized subgraph inputs.

Conclusions:

  • KGMP offers a novel technical paradigm for effective collaboration between LLMs and knowledge graphs.
  • The proposed framework significantly improves KBQA performance, particularly for complex multi-hop queries.
  • Dynamic graph traversal and optimized subgraph representation are key to advancing KBQA capabilities.