A curated, ontology-based, large-scale knowledge graph of artificial
  intelligence tasks and benchmarks
  Research in artificial intelligence (AI) is addressing a growing number of
tasks through a rapidly growing number of models and methodologies. This makes
it difficult to keep track of where novel AI methods are successfully -- or
still unsuccessfully -- applied, how progress is measured, how different
advances might synergize with each other, and how future research should be
prioritized.
  To help address these issues, we created the Intelligence Task Ontology and
Knowledge Graph (ITO), a comprehensive, richly structured and manually curated
resource on artificial intelligence tasks, benchmark results and performance
metrics. The current version of ITO contain 685,560 edges, 1,100 classes
representing AI processes and 1,995 properties representing performance
metrics.
  The goal of ITO is to enable precise and network-based analyses of the global
landscape of AI tasks and capabilities. ITO is based on technologies that allow
for easy integration and enrichment with external data, automated inference and
continuous, collaborative expert curation of underlying ontological models. We
make the ITO dataset and a collection of Jupyter notebooks utilising ITO openly
available.
