HACID bases the hybrid collective intelligence approach for decision support on formal knowledge about an application domain structured in a Domain Knowledge Graph (DKG). The DKG describes domain knowledge in terms of concepts and relations (e.g. the Legionnaires' disease has the Legionella as causative agent). Thanks to the DKG, it is possible to identify concepts that are relevant for a specific case, hence obtaining the so-called Case Knowledge Graph (CKG), which can be exploited to support the hybrid collective intelligence approach.
HACID has developed a DKG for medical diagnostics by repurposing SNOMED-CT, a comprehensive collection of medical terms that is used as a reference terminology worldwide for the medical domain. The work performed on SNOMED-CT enables a richer representation of the information by making explicit the semantic information included in SNOMED-CT as concept attributes. Additionally, additional data is linked from available databases (e.g. abbreviations) and possibly also open-source repositories (e.g. Wikipedia). Finally, methodologies have been developed for linking supporting evidence from scientific and grey literature, so that user can navigate the DKG and find relevant information and support for decision making.
HACID is also developing a DKG for climate change adaptation management, which represents a relevant resource for a continuously growing application domain. Differently from medical diagnostics, for climate services there is no readily available resource that encompasses all relevant concepts for climate science and service provision, hence it has been necessary to design a new model ontology in strict collaboration with climate service experts. The available datasets from intercomparison projects like CMIP6 or from repositories such as Copernicus offer invaluable sources of information for the DKG, which however need to be harmonised into the devised semantic model. Also, evidence from international reports (e.g. IPCC) needs to be linked to the DKG through automatic information extraction methods. Once these challenges have been successfully tackled, HAVID will rely on a large knowledge base on which to provide decision support for climate services.
Besides building the DKGs, HACID also developed general-purpose methods for automatic identification of relevant information about specific cases in order to obtain the CKG, as well as interfaces for knowledge exploration and semantic annotation that can help experts enrich the available information about a case directly through the KG structure. Finally, methods have been developed for the aggregation of solutions crowdsources from domain experts, exploiting the CKG as the reference resource on which to match and aggregate suggested concepts.
The research and innovation activities have been performed with a user-centric perspective, taking into account stakeholders' needs from the very beginning. User research has allowed us to surface the needs of experts in both medical diagnostics and climate services, suggesting how a decision support system can contribute to daily activities. Then, opportunities for hybrid collective intelligence have been scoped out, indicating the most promising aspects to be investigated within HACID. These have been just the first steps moved by our Participatory AI approach that promises inclusion of stakeholders needs and opinions in every part of the technology development.
