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research-article

Medical knowledge graph completion via fusion of entity description and type information

Authors:

Xiaomin ZhuAuthors Info & Claims

Volume 151, Issue C

https://doi.org/10.1016/j.artmed.2024.102848

Published: 01 May 2024 Publication History

Abstract

Medical Knowledge Graphs (MKGs) are vital in propelling big data technologies in healthcare and facilitating the realization of medical intelligence. However, large-scale MKGs often exhibit characteristics of data sparsity and missing facts. Following the latest advances, knowledge embedding addresses these problems by performing knowledge graph completion. Most knowledge embedding algorithms rely solely on triplet structural information, overlooking the rich information hidden within entity property sets, leading to bottlenecks in performance enhancement when dealing with the intricate relations of MKGs. Inspired by the semantic sensitivity and explicit type constraints unique to the medical domain, we propose BioBERT-based graph embedding model. This model represents an evolvable framework that integrates graph embedding, language embedding, and type information, thereby optimizing the utility of MKGs. Our study utilizes not only WordNet as a benchmark dataset but also incorporates MedicalKG to compare and corroborate the specificity of medical knowledge. Experimental results on these datasets indicate that the proposed fusion framework achieves state-of-art (SOTA) performance compared to other baselines. We believe that this incremental improvement provides promising insights for future medical knowledge graph completion endeavors.

Highlights

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A novel fusion framework is introduced for medical knowledge graph completion.

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Our methodology incorporates a type-sensitive learning strategy that ensure higher precision.

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We improve link prediction with an ontology language model and if-else logic.

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Research shows that the medical knowledge graph is more sensitive to semantic information.

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Our method surpasses baselines in WordNet and MedicalKG, highlighting its applicability.

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Cited By

Susanti FMaulidevi NSurendro K(2024)Improving embedding-based link prediction performance using clusteringJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10218136:8Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.jksuci.2024.102181

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Information & Contributors

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Published In

cover image Artificial Intelligence in Medicine

Artificial Intelligence in Medicine Volume 151, Issue C

May 2024

297 pages

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Elsevier B.V.

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Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 May 2024

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Cited By

Susanti FMaulidevi NSurendro K(2024)Improving embedding-based link prediction performance using clusteringJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10218136:8Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.jksuci.2024.102181

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