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King Saud Univ. Comput. Inf. Sci."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Evidence serves as the basis for determining facts in the judicial trial process, and exploring the correlation between evidence has become an essential task. However, there is uncertainty and unreliability of evidence. In this paper, we propose an ApriDS model that can mine correlations between evidence and construct evidence linkage networks. Specifically, we use the association rule algorithm to mining for the correlations between evidence in order to initially construct an evidence linkage network. Moreover, we also combine the imprecise reasoning theory to alleviate unreliability among evidence. Finally, combining the above effectively mitigates uncertainty and unreliability in the evidence linkage network. We conduct extensive experiments and present analyses on five case datasets, including marriages, thefts, homicides, contract disputes and unlawful detention. Compared to the baseline model, our method\u2019s accuracy improves by 12.75%, 14.16%, 14.80%, 21.64% and 26.15% on the five datasets, respectively.<\/jats:p>","DOI":"10.1007\/s44443-025-00020-7","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T09:11:04Z","timestamp":1742289064000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ApriDS: An evidence linkage network construction model for judgment documents"],"prefix":"10.1007","volume":"37","author":[{"given":"Yulin","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1960-8628","authenticated-orcid":false,"given":"Yongbin","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117436","volume":"202","author":"T Agouti","year":"2022","unstructured":"Agouti T (2022) Graph-based modeling using association rule mining to detect influential users in social networks. 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