Title: Intelligent retrieval method for legal information based on semantic knowledge graph

Authors: Yuxia Gao

Addresses: Zhengzhou Sias University, Xinzheng, Henan, China

Abstract: To improve the average accuracy of retrieval results and the normalised discounted cumulative gain, this paper proposes an intelligent legal information retrieval method based on a semantic knowledge graph. First, the quality of legal information is ensured through data pre-processing. Next, natural language processing techniques are used to extract semantic features from legal information and to establish correlations among these features. Subsequently, a legal knowledge graph is constructed through entity recognition and relationship extraction, integrating knowledge from multiple data sources to form a comprehensive legal information association model. Finally, the Lagrange algorithm is applied to achieve intelligent retrieval. Experimental results show that the average accuracy of the proposed method remains above 0.95, while the normalised discounted cumulative gain fluctuates between 0.962 and 0.991, demonstrating its effectiveness and practicality. The proposed method provides legal practitioners with a faster and more accurate tool for legal information retrieval.

Keywords: legal information; intelligent retrieval; semantic features; knowledge graph; Lagrange algorithm.

DOI: 10.1504/IJIPT.2026.152212

International Journal of Internet Protocol Technology, 2026 Vol.19 No.1, pp.25 - 33

Received: 24 Mar 2025
Accepted: 24 May 2025
Published online: 11 Mar 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article