Guided Navigation in Knowledge-Dense Environments: Structured Semantic Exploration with Guidance Graphs

2025-08-06

AI Summary

Large language models (LLMs) rely on static knowledge and exhibit opaque reasoning in knowledge-intensive tasks; existing knowledge graph (KG)-enhanced methods suffer from either granularity mismatch (query-guided approaches) or insufficient contextual utilization (clue-guided approaches). Method: We propose the Guided Graph framework, which constructs an intermediate, structured guided graph that jointly integrates query- and clue-guided exploration. It enables structure-aligned filtering and context-aware pruning to achieve dynamic, interpretable knowledge retrieval. Crucially, target knowledge is abstracted as a semantically coherent guided graph, and contextual constraints are explicitly modeled during retrieval. Contribution/Results: Experiments demonstrate significant improvements over state-of-the-art methods on complex multi-hop question answering, with consistent efficiency and robustness even on medium- and small-scale LLMs. The framework combines theoretical rigor with practical applicability.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Question Answering

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

While Large Language Models (LLMs) exhibit strong linguistic capabilities, their reliance on static knowledge and opaque reasoning processes limits their performance in knowledge intensive tasks. Knowledge graphs (KGs) offer a promising solution, but current exploration methods face a fundamental trade off: question guided approaches incur redundant exploration due to granularity mismatches, while clue guided methods fail to effectively leverage contextual information for complex scenarios. To address these limitations, we propose Guidance Graph guided Knowledge Exploration (GG Explore), a novel framework that introduces an intermediate Guidance Graph to bridge unstructured queries and structured knowledge retrieval. The Guidance Graph defines the retrieval space by abstracting the target knowledge' s structure while preserving broader semantic context, enabling precise and efficient exploration. Building upon the Guidance Graph, we develop: (1) Structural Alignment that filters incompatible candidates without LLM overhead, and (2) Context Aware Pruning that enforces semantic consistency with graph constraints. Extensive experiments show our method achieves superior efficiency and outperforms SOTA, especially on complex tasks, while maintaining strong performance with smaller LLMs, demonstrating practical value.

Problem

• LLMs struggle with knowledge-intensive tasks due to static, opaque reasoning
• Current KG exploration methods face trade-offs between redundancy and context utilization
• Need for precise knowledge retrieval bridging unstructured queries and structured KGs

Innovation

• Guidance Graph bridges queries and knowledge retrieval
• Structural Alignment filters candidates efficiently
• Context Aware Pruning enforces semantic consistency