Imagine having a team of top experts and a super-intelligent library that can read every medical or climate paper ever written. This system helps them work together to solve tricky problems where there isn't one single right answer. It combines human intuition with AI's ability to scan massive amounts of data to find the best possible solution.
Professionals in high-stakes fields often struggle with 'open-ended' problems where no single correct answer exists and the available evidence is scattered across vast amounts of literature.
A decision support system (HACID-DSS) that uses Domain Knowledge Graphs to aggregate human expert advice and AI-generated suggestions into a single, traceable solution.
If you are a diagnostic clinic dealing with rare diseases and complex symptoms — this project developed a decision support system that aggregates expert opinions and AI data to identify the most likely condition. This improves accuracy in cases where a single doctor might miss a rare connection.
If you are an urban planning firm dealing with climate change adaptation for cities — this project developed a tool that matches specific service requests with the latest climate science and socio-economic data. It helps planners make evidence-based decisions for city infrastructure.
If you are a professional services firm dealing with high-stakes, open-ended problems — this project developed a hybrid intelligence system that turns scientific literature into a searchable knowledge graph. This allows your team to reach a collective solution faster and with better traceability.
Based on available project data, no specific commercial pricing or licensing costs are mentioned; the project was funded by an EU contribution of EUR 1,877,250.
The project aims to develop a general methodology applied to medical and climate domains, suggesting it is designed for scalability across different high-stakes professional fields.
Based on available project data, specific IP or licensing agreements are not detailed in the summary.
The system integrates data by semi-automatically assembling Domain Knowledge Graphs from scientific and gray literature.
The project period runs from 2022-09-01 to 2026-02-28, indicating it is currently in the development and testing phase.
The consortium consists of 5 partners across 4 countries (DE, IE, IT, UK). It is heavily research-driven with 3 research organizations and 1 university, but includes 1 SME, bringing the industry ratio to 20%. This suggests a strong academic foundation with a targeted path toward commercial application via the SME partner.
Contact the Consiglio Nazionale delle Ricerche (CNR) in Italy.
Contact us to explore licensing the HACID-DSS for your professional services.
