# Judgment2vec: Apply Graph Analytics to Searching and Recommendation of Similar Judgments

arXiv: https://arxiv.org/abs/2408.04382 · HTML: https://mlsanity.com/paper/2408.04382/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020)
- [Methods for Computing Legal Document Similarity: A Comparative Study](https://mlsanity.com/paper/2004.12307/) (2020)

## Abstract (arXiv)

In court practice, legal professionals rely on their training to provide opinions that resolve cases, one of the most crucial aspects being the ability to identify similar judgments from previous courts efficiently. However, finding a similar case is challenging and often depends on experience, legal domain knowledge, and extensive labor hours, making veteran lawyers or judges indispensable. This research aims to automate the analysis of judgment text similarity. We utilized a judgment dataset labeled as the "golden standard" by experts, which includes human-verified features that can be converted into an "expert similarity score." We then constructed a knowledge graph based on "case-article" relationships, ranking each case using natural language processing to derive a "Node2vec similarity score." By evaluating these two similarity scores, we identified their discrepancies and relationships. The results can significantly reduce the labor hours required for legal searches and recommendations, with potential applications extending to various fields of information retrieval.