Knowledge graph embedding and reasoning for real-time analytics support of chemical diagnosis from exposure symptoms

Chemical exposure accidents pose a risk of serious injury and property damage if the diagnosis or response is not properly performed after the initial discovery. Due to lack of research on the dynamically changing environment and detection of chemical substances considering symptoms, real-time knowledge services are required, such as rapid diagnosis of chemicals exposed at the accident site and the following early response. In this study, we propose an AI-based analysis system, Symptom-based Expert for Advanced Response to Chemical Hazards (SEARCH), for chemical substance diagnosis from exposure symptoms actively collected for real-time response and mitigation to hazardous material accidents. Knowledge is collected from chemical database such as WISER, PubChem etc., and integrated for the analytics of chemical exposure accidents and contact symptoms. We design and construct ontology and knowledge graph (KG) for 1001 major chemical substances. The built KG is verified using KG embedding models and the performance of each model is compared. The proposed system identifies the substance candidates through KG query and reasoning considering the exposure conditions. Using the symptom KG, the system SEARCH can provide the means to analyze real-time data from the field and transform it into insights and actions related to emergency response.