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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks
Kathrin Blagec1, Adriano Barbosa-Silva1, Simon Ott1
1Medical University of Vienna, Center for Medical Statistics, Informatics and Intelligent Systems, Institute of Artificial Intelligence, Vienna, Austria.
Scientific Data
|June 17, 2022
Summary
We developed the Intelligence Task Ontology and Knowledge Graph (ITO), a structured resource for tracking artificial intelligence (AI) tasks and performance metrics. ITO facilitates analysis of the AI research landscape and aids in prioritizing future AI advancements.
Area of Science:
- Artificial Intelligence
- Knowledge Representation
- Ontology Engineering
Background:
- The rapid expansion of artificial intelligence (AI) models and methodologies presents challenges in tracking progress and identifying research priorities.
- Existing resources lack a comprehensive, structured overview of AI tasks, benchmarks, and performance metrics.
Purpose of the Study:
- To introduce the Intelligence Task Ontology and Knowledge Graph (ITO) as a centralized, manually curated resource for AI research.
- To enable systematic analysis of the global AI task landscape and facilitate informed research prioritization.
Main Methods:
- Development of a richly structured ontology and knowledge graph manually curated by experts.
- Integration of AI tasks, benchmark results, and performance metrics within the ITO framework.
- Utilization of technologies supporting data integration, automated inference, and collaborative curation.
Main Results:
- The ITO currently comprises 1,100 classes for AI processes and 1,995 properties for performance metrics, with 685,560 edges.
- The resource provides a detailed map of AI tasks and their associated performance metrics.
- Openly available dataset and Jupyter notebooks for utilizing ITO.
Conclusions:
- ITO offers a foundational resource for understanding and navigating the complex AI research ecosystem.
- The structured nature of ITO supports advanced analytics, synergy identification, and strategic research planning in AI.
- Open accessibility promotes collaborative development and broader adoption within the AI community.
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