Project
NOURISH
Conversational public-health data exploration with knowledge graphs and secure data services.
Overview
NOURISH is a useful example of the site’s AI and data-systems overlap: the point is not only retrieval, but the semantics, governance, and workflow around discovery.
Placeholder notes
NOURISH fits the ADIL research profile around heterogeneous data integration, domain-specific knowledge graph construction, knowledge-based conversational engines, and security/privacy in complex data.
Project details
Problem: How can people ask complex public-health questions without requiring deep technical knowledge of the underlying systems?
Approach: Connected knowledge-based conversational engines to heterogeneous data integration and privacy-aware access patterns.
Contributions
- Supported secure data-service design.
- Contributed to knowledge-graph thinking for discovery.
- Helped make multimodal health data easier to explore.
Outcomes
- Aligned with the lab's AI-enabled query processing and decision-support direction.