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Biomedical knowledge graph verification with multitask learning architectures
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DOI：10.1016/j.jbi.2025.104894.png)
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Abstract
En 中文
Large-scale biomedical KGs, typically constructed using automated entity-relation extraction methods from vast amounts of textual documents, often contain erroneous biomedical triplets, which raises concerns about their quality. Using such noisy KGs in downstream applications can compromise the validity of biomedical research and lead to inaccurate conclusions. This study aims to desig n an effective knowledge graph verification (KGV) method to determine the correctness of triplets in biomedical KGs, enabling the removal of erroneous triplets identified through the proposed method.
Keywords:
knowledge graph verification
biomedical knowledge graphs
triplet correctness
noisy knowledge graphs
entity-relation extraction
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NEUROCOMPUTINGIF6.5
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ShareSaveShareSaveShareSaveKnowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question AnsweringProceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)
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Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)IF0
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