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Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label.

Guangya Yu1,2, Qi Ye2, Tong Ruan2

  • 1Zhejiang Laboratory, Hangzhou 311121, China.

Bioengineering (Basel, Switzerland)
|March 27, 2024
PubMed
Summary

This study introduces EMKGEL, a novel method for enhancing error detection in medical knowledge graphs (MKGs) by using intrinsic label information. EMKGEL improves the accuracy of MKGs, crucial for reliable healthcare applications.

Keywords:
confidence scoreerror detectiongraph attention networkmedical knowledge graph

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Area of Science:

  • Bioinformatics
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Medical knowledge graphs (MKGs) are increasingly built using automated methods, introducing noise that can degrade healthcare application performance.
  • Current error detection methods rely on graph topology and external entity labels, which are costly to obtain accurately.
  • The lack of precise entity labels in MKGs hinders the improvement of their quality.

Purpose of the Study:

  • To propose Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL), an approach to improve MKG quality.
  • To address the limitations of existing error detection methods by utilizing intrinsic label information.
  • To enhance the reliability and performance of downstream healthcare applications that utilize MKGs.

Main Methods:

  • Constructed a hyper-view knowledge graph (KG) for implicit label information and a triplet-level KG for neighborhood information.
  • Introduced a hyper-view graph attention network (GAT) to integrate label messages and neighborhood data for representation learning.
  • Developed a confidence score combining local and global trustworthiness to estimate triplet accuracy.

Main Results:

  • EMKGEL demonstrated improved Precision@K values by 0.7% on PharmKG-8k, 6.1% on DiseaseKG, and 3.6% on DiaKG.
  • The approach significantly outperformed baseline methods on three public MKGs and a general knowledge graph (Nell-995).
  • The proposed method effectively leverages intrinsic label information for more accurate error detection in MKGs.

Conclusions:

  • EMKGEL offers a robust solution for detecting errors in MKGs, particularly when precise external labels are unavailable.
  • The integration of hyper-view GAT and a trustworthiness-based confidence score enhances the quality of MKGs.
  • This work contributes to building more reliable and accurate medical knowledge graphs for advanced healthcare applications.