用超关系提升大模型在知识图谱中的推理效率与准确率
Reasoning of Large Language Models over Knowledge Graphs with Super-Relations
- 引入超关系整合多种路径，支持正向与反向推理
- 在9个数据集上平均准确率提升2.92%
- 适合需要高效知识推理的AI系统开发者
尽管大语言模型在知识图谱处理与推理方面取得显著进展，但现有方法存在高非检索率问题，影响问答准确性。分析表明，贪婪搜索与正向推理的结合是主要成因。为此，我们提出超关系概念，通过归纳和连接图中多种关系路径，实现正向与反向推理。该方法不仅扩大搜索空间，还显著提升检索效率。本文提出ReKnoS框架，通过超关系融合多条关系路径，增强前后向推理能力，并提高对大模型查询的效率。在九个真实世界数据集上的大量实验表明，相比现有最优基线，ReKnoS平均准确率提升2.92%，显著改善了推理性能。
原文摘要 · Abstract (English)
While large language models (LLMs) have made significant progress in processing and reasoning over knowledge graphs, current methods suffer from a high non-retrieval rate. This limitation reduces the accuracy of answering questions based on these graphs. Our analysis reveals that the combination of greedy search and forward reasoning is a major contributor to this issue. To overcome these challenges, we introduce the concept of super-relations, which enables both forward and backward reasoning by summarizing and connecting various relational paths within the graph. This holistic approach not only expands the search space, but also significantly improves retrieval efficiency. In this paper, we propose the ReKnoS framework, which aims to Reason over Knowledge Graphs with Super-Relations. Our framework's key advantages include the inclusion of multiple relation paths through super-relations, enhanced forward and backward reasoning capabilities, and increased efficiency in querying LLMs. These enhancements collectively lead to a substantial improvement in the successful retrieval rate and overall reasoning performance. We conduct extensive experiments on nine real-world datasets to evaluate ReKnoS, and the results demonstrate the superior performance of ReKnoS over existing state-of-the-art baselines, with an average accuracy gain of 2.92%.
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