  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.
