Enabling machine-actionable semantics for comparative analyses of trait evolution (SCATE)

We aim to enable comparative trait analyses that are powered by computational inferences and machine reasoning based on the meanings of trait descriptions. We will do this by addressing three long-standing limitations in comparative studies of trait evolution: recombining trait data, modeling trait evolution, and generating testable hypotheses for the drivers of trait adaptation. We will create a centralized computational infrastructure that affords comparative analysis tools the ability to compute with morphological knowledge through scalable online application programming interfaces (APIs), enabling developers of comparative analysis tools, and therefore their users, to tap into machine reasoning-powered capabilities and data with machine-actionable semantics. To accomplish this, the project will adapt key products and know-how developed by the Phenoscape project, including an integrative knowledgebase of ontology-linked phenotype data (the Phenoscape KB), metrics for quantifying the semantic similarity of phenotype descriptions, and algorithms for synthesizing morphological data from published trait descriptions.

Jim Balhoff is Co-PI and leads semantic technology infrastructure development for the project.

• Battelle Memorial Institute
• Duke University
• UNC Department of Biology
• University of South Dakota
• Virginia Polytechnic Institute and State University

• National Science Foundation