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Knowledge Graph (KG) contains entities and the relations between entities.Due to its representation ability, KG has been successfully applied to supportmany medical/healthcare tasks. However, in the medical domain, knowledge holdsunder certain conditions. For example, symptom \emph{runny nose} highlyindicates the existence of disease \emph{whooping cough} when the patient is ababy rather than the people at other ages. Such conditions for medicalknowledge are crucial for decision-making in various medical applications,which is missing in existing medical KGs. In this paper, we aim to discoverymedical knowledge conditions from texts to enrich KGs.  Electronic Medical Records (EMRs) are systematized collection of clinicaldata and contain detailed information about patients, thus EMRs can be a goodresource to discover medical knowledge conditions. Unfortunately, the amount ofavailable EMRs is limited due to reasons such as regularization. Meanwhile, alarge amount of medical question answering (QA) data is available, which cangreatly help the studied task. However, the quality of medical QA data is quitediverse, which may degrade the quality of the discovered medical knowledgeconditions. In the light of these challenges, we propose a new truth discoverymethod, MedTruth, for medical knowledge condition discovery, which incorporatesprior source quality information into the source reliability estimationprocedure, and also utilizes the knowledge triple information for trustworthyinformation computation. We conduct series of experiments on real-world medicaldatasets to demonstrate that the proposed method can discover meaningful andaccurate conditions for medical knowledge by leveraging both EMR and QA data.Further, the proposed method is tested on synthetic datasets to validate itseffectiveness under various scenarios.

MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data