Thanks to technological advances, animal geneticists have an ever-expanding tool chest with which to study the inheritance of traits in livestock in order to improve production. Our long-range goal is to develop integrated resources that leverage prior investments in cyberinfrastructure to help maximize the utility of genotype-to-phenotype data to functionally annotate livestock genomes. The objectives of this particular application are: 1) development of machine learning-assisted data curation and automated semantic annotation, and 2) manual curation of genotype/phenotype, correlation, and heritability data. With the growing volume and breadth of information, it is increasingly difficult for curators to keep abreast of publications. These complementary objectives target the need to efficiently collect and comprehend large amounts of genotype/phenotype association and correlation/heritability data that are being published at an accelerating rate. First, we expect to begin to automate the functional annotation of livestock genomes by applying artificial intelligence techniques to the curation of published QTL/variant association data into the Animal QTLdb, and genetic and phenotypic correlation and heritability data into the Animal CorrDB, for multiple livestock species. Second, we expect to develop artificial intelligence tools to expedite ontology development. Third, we expect to develop intelligent retrieval tools that can answer queries semantically. Fourth, we expect to curate genotype/phenotype and correlation/heritability data and to expand relevant ontologies. Taken together, our efforts are expected to generate positive long-term effects on researchers' ability to transfer knowledge and analyze QTL/association data to address issues of economic and health importance in livestock species.