Session Organizers:

  • Jim Balhoff, Renaissance Computing Institute, University of North Carolina at Chapel Hill
  • Hilmar Lapp, Center for Genomic and Computational Biology, Duke University
  • Chris Mungall, Lawrence Berkeley National Lab


Ontologies allow the encoding of knowledge domains with formal semantics such that automated reasoning software can make powerful inferences— facts implied by, but not asserted in the encoded body of knowledge. When linked to domain-specific natural-language observations this can enable the discovery of otherwise obscured connections and allow quantitative evaluation of the semantic relatedness of knowledge concepts, and thus the data linked to them. By allowing various probabilistic and statistical approaches to data mining, these capabilities directly support discovery, such as in the form of generating testable hypotheses automatically and at scale. Although presenting potentially big opportunities, applications of formal reasoning are also hampered by hard challenges and trade-offs, such as between logical expressivity and scaling reasoner performance, and a tool ecosystem with many gaps. This session aims to raise awareness of both opportunities and challenges, and to coalesce the otherwise disparate community around tackling common gaps.