When Truth Discovery Meets Medical Knowledge Graph: Estimating Trustworthiness Degree for Medical Knowledge Condition

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.