The emergence of large language models (LLMs) presents an unprecedented opportunity to automate construction contract management, reducing human errors and saving significant time and costs. However, LLMs may produce convincing yet inaccurate and misleading content due to a lack of domain expertise. To address this issue, expert-driven contract knowledge can be represented in a structured manner to constrain the automatic contract management process. This paper introduces the Nested Contract Knowledge Graph (NCKG), a knowledge representation approach that captures the complexity of contract knowledge using a nested structure. It includes a nested knowledge representation framework, a NCKG ontology built on the framework, and an implementation method. Furthermore, we present the LLM-assisted contract review pipeline enhanced with external knowledge in NCKG. Our pipeline achieves a promising performance in contract risk reviewing, shedding light on the combination of LLM and KG towards more reliable and interpretable contract management.

翻译：大语言模型（LLMs）的出现为自动化施工合同管理带来了前所未有的机遇，可减少人为错误并大幅节省时间与成本。然而，由于缺乏领域专业知识，LLMs可能生成看似合理但实际不准确且具有误导性的内容。为解决这一问题，可采用结构化方式表示专家驱动的合同知识，以约束自动化合同管理过程。本文提出嵌套合同知识图谱（NCKG），这是一种利用嵌套结构捕捉合同知识复杂性的知识表示方法，包括嵌套知识表示框架、基于该框架构建的NCKG本体及其实现方法。此外，我们展示了通过NCKG外部知识增强的LLM辅助合同审查流水线。该流水线在合同风险审查中取得了显著成效，为融合LLM与知识图谱实现更可靠、可解释的合同管理提供了新思路。