Call for Submissions¶

1. Ontological Foundations for Trustworthy AI¶

• Methods for grounding Large Language Models (LLMs) in formal ontologies
• Advancements in neuro-symbolic AI and explainable AI (XAI)
• Ensuring logical consistency in AI-mediated knowledge discovery

2. Semantic Modeling and Bounding of GenAI¶

• Ontology-driven validation of generative outputs
• Semantic approaches to hallucination detection and mitigation
• Integration of Knowledge Graphs (KGs) with generative workflows

3. Domain-Specific Applications in Biomedical Research¶

Practical implementations across core domains:

• Life Sciences: Molecular biology, genomics, and proteomics discovery
• Clinical & Public Health: Heterogeneous data integration for epidemiology and health informatics
• Wellness & Behavior: Frameworks for mental health, physical wellness, environmental health, and behavioral science
• Global Systems: Food safety, agricultural systems, and environmental sustainability

4. AI-Readiness and Semantic Infrastructure¶

• Defining and benchmarking "AI-readiness" for datasets and ontologies
• Governance, sustainability, and community standards evolution
• Semantic metadata's role in model training and fine-tuning

5. FAIR Principles in the Era of GenAI¶

• Evaluating FAIR principles sufficiency for machine-actionable data
• Provenance, traceability, and reproducibility in AI-integrated pipelines

6. Practical and Theoretical Perspectives¶

• Distinctions between Semantics and Ontology in GenAI contexts
• The future of biocuration and manual knowledge engineering