AI-Ready Data: Navigating the Dynamic Frontier of Metadata and Ontologies

ID4: Institute of Data Driven Dynamical Design

Hosted by the Metadata Research Center, College of Computing & Informatics, Drexel University

AI-ready data refers to the high-quality and well-prepared data that is optimized for use in artificial intelligence (AI) applications. AI-ready data increasingly encompasses the inclusion of metadata and ontologies to enhance the value and usability of data. Metadata provides essential context and information about the data, and ontologies offer structured semantic representation of a particular domain. These additional layers of information help data scientists,data scientists, researchers, and AI systems understand, interpret, and apply appropriate algorithms and models for analysis. Metadata and ontologies enable consistent data integration, interoperability, and knowledge sharing across systems, while facilitating more knowledgeable AI applications. Additionally, these systems are proving vital for supporting the FAIR (Findable, Accessible, Interoperable, and Reusable) principles and reproducible computational research (RCR).

Despite these capacities, approaches for developing, implementing, and sustaining metadata and ontologies within AI-ready data pipelines remain inconsistent, cumbersome, and lack sufficient support. Challenges underlie the full data lifecycle from data creation, collection, and research, to longer-term aims of data preservation, archiving, reuse and support for research reproducibility. Collective, community driven efforts are needed to address current obstacles and maximize the value and reliability of data. The AI-Ready Data: Navigating the Dynamic Frontier of Metadata and Ontologies workshop is a step toward addressing this challenge. This workshop will bring together a community of individuals with expertise across the data lifecycle to discuss issues, share solutions, and chart a path forward for addressing key challenges in preparing AI-ready data for scientific research. 

Specific workshop goals are to:

  1. Collectively define the state of AI-ready data challenges in the metadata and ontology space
  2. Share current successes and solutions leveraging metadata standards and ontologies.
  3. Contribute to a road map to accelerate the preparation of data for artificial intelligence (AI) applications.

Current topics

  • What is AI ready data
  • Research Bottlenecks: Data Life Cycle Challenges and Solutions with Scientific Data
  • Metadata and Ontologies: Human in the Loop in the Era of LLMs
  • Annotation: Large-scale Data and Balancing Human and Machine Driven Approaches 
  • Standards Development, Adoption, and Implementation: Realities and Fictions
  • Knowledge Graphs
  • Ontology Guided Knowledge Extraction: Leveraging Scholarly Big Data for Scientific Discovery
  • Future Directions with Metadata and Knowledge Organization Systems