EAGER: Medical Knowledge Graph Construction from Heterogeneous Sources

The objective of this project is to construct comprehensive medical knowledge graphs. Such knowledge graphs can satisfy users' growing needs for reliable medical information, help the effective communication between patients and doctors, and potentially help reduce the high costs of health care. This project tackles a series of unique challenges observed in medical domains, and develops effective approaches that can extract knowledge from the deluge of crowdsourced data to augment medical knowledge graphs. The proposed research advances the fields of knowledge graph construction and information trustworthiness analysis by developing novel methods that mines knowledge from unstructured data in medical domains. This project investigates the problem of medical graph construction from the following perspectives: 1) Effective approaches are developed to take into account the semantic relations between medical terms during the extraction of reliable medical facts from noisy answers on healthcare question and answering websites; 2) A unified framework is designed to integrate information from heterogeneous data sources in the process of medical knowledge discovery; 3) The knowledge graph is completed by inferring new relations based on existing relations between medical terms in the graph. The proposed research is implemented into a system prototype that displays the extracted medical facts and the knowledge graph, which benefit online users who seek medical information. The proposed techniques are used to enhance educational methodologies. Research results of this project are integrated into course materials and projects that reinforce student training.