Ontology Construction and Pattern Matching for Contract Risk Assessment

Abstract

Contract review is a significant burden for legal professionals, and various AI tools are being developed to address this. Recent studies have reported that large language models (LLMs) demonstrate higher accuracy than junior lawyers and can significantly reduce costs. However, when using AI to assist humans, the interpretability of outputs generated by LLMs becomes a challenging issue.This paper proposes a pattern matching method aimed at improving interpretability by predefining risk-related expressions using an ontology and matching them with problematic parts of input sentences to determine risks. Experimental results show that while the proposed method falls short by about 10 points in risk determination accuracy compared to a prompt-based classification method, it is capable of making risk determinations with a certain level of accuracy.In conclusion, we demonstrate that a hybrid approach is promising, where the initial risk determination is achieved with high accuracy using the prompt-based classification method, and the interpretability is supported by the proposed method through verification against predefined criteria for the determined risks.