Knowledge Representation

Artificial Intelligence applications require Knowledge Representation and Reasoning to encode knowledge in a way that computers can reason with. The Knowledge Representation group at CCB develops ontology-based solutions to facilitate the annotation, search, and meta-analysis of biomedical data originating from diverse sources. The data include electronic health records, healthcare insurance claims, genomics, environmental exposure, among others. The integration of these data will support large-scale epidemiological research in precision medicine, healthcare, and basic science, all with the goal of improving patient outcomes. Our expertise includes ontology engineering (design, implementation, debugging, querying, and visualization), RDF triple stores, graph databases, linked data, semantic web technologies, data standardization, software engineering, machine learning, and reasoning.

For questions about CCB’s Knowledge Representation work group or to ask how CCB can help with your project, please email CCB.