ArticleScientific data2022
A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- Business Intent and Network Slicing Correlation Dataset from Data-Driven Perspective.Scientific data · 2025Article
- AI Thinking: a framework for rethinking artificial intelligence in practice.Royal Society open science · 2025Article
- Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label.Bioengineering (Basel, Switzerland) · 2024Article
- Review
- Advancing the Management of Long COVID by Integrating into Health Informatics Domain: Current and Future Perspectives.International journal of environmental research and public health · 2023Review
- Current status and frontier tracking of the China HACCP system.Frontiers in nutrition · 2023Review
- Mapping global dynamics of benchmark creation and saturation in artificial intelligence.Nature communications · 2022Article
- A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks.Scientific data · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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 contains 685,560 edges, 1,100 classes representing AI processes and 1,995 properties representing performance metrics. The primary goal of ITO is to enable 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 utilizing ITO openly available.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.