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• W2893531633 abstract "Medical knowledge graph is the core component for various medical applications such as automatic diagnosis and question-answering. However, medical knowledge usually associates with certain conditions, which can significantly affect the performance of the supported applications. In the light of this challenge, we propose a new truth discovery method to explore medical-related texts and infer trustworthiness degrees of knowledge triples associating with different conditions. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed truth discovery method." @default.
• W2893531633 created "2018-10-05" @default.
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• W2893531633 date "2018-09-27" @default.
• W2893531633 modified "2023-09-22" @default.
• W2893531633 title "When Truth Discovery Meets Medical Knowledge Graph: Estimating Trustworthiness Degree for Medical Knowledge Condition." @default.
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