文献类型：期刊文献

英文题名：Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label

作者：Yu, Guangya[1,2];Ye, Qi[2];Ruan, Tong[2]

机构：[1]Zhejiang Lab, Hangzhou 311121, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Technol, Shanghai 200237, Peoples R China

年份：2024

卷号：11

期号：3

外文期刊名：BIOENGINEERING-BASEL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001191582700001)】；

基金：No Statement Available

语种：英文

外文关键词：medical knowledge graph; error detection; confidence score; graph attention network

摘要：The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL). Considering the absence of hyper-view KG, we establish a hyper-view KG and a triplet-level KG for implicit label information and neighborhood information, respectively. Inspired by the success of graph attention networks (GATs), we introduce the hyper-view GAT to incorporate label messages and neighborhood information into representation learning. We leverage a confidence score that combines local and global trustworthiness to estimate the triplets. To validate the effectiveness of our approach, we conducted experiments on three publicly available MKGs, namely PharmKG-8k, DiseaseKG, and DiaKG. Compared with the baseline models, the Precision@K value improved by 0.7%, 6.1%, and 3.6%, respectively, on these datasets. Furthermore, our method empirically showed that it significantly outperformed the baseline on a general knowledge graph, Nell-995.