Extracting Core Meaning from Legal Queries Using Semantic Technologies

Abstract

Legal queries are often expressed in unstructured, ambiguous, or noisy natural language, which poses significant challenges for accurate information retrieval. This paper presents a comprehensive framework for legal question answering that integrates semantic technologies - including an ontology platform, knowledge graph, and large language models (LLMs) - to improve question understanding and response accuracy. We propose a multi-step pre-processing pipeline that standardizes legal questions using spelling correction, abbreviation expansion, syntactic restructuring, and semantic summarization supported by LLMs. Besides, a query system is designed that maps pre-processed questions into graph-based representations and performs subgraph matching over a legal knowledge graph. The system is evaluated on real-world legal questions collected from online forums covering traffic law and social insurance. The results show that the proposed approach achieves a high semantic similarity score (avg. cosine similarity of 0.9078 after standardization) and outperforms baseline LLMs like ChatGPT and Gemini in query accuracy (up to 82.25% in certain question categories). These findings highlight the effectiveness of combining LLMs and semantic structures for robust legal information retrieval.