In disaster risk management, information often comes under the shape of knowledge graphs with different detail levels across multiple disciplines. And not only humans should be able to analyse and digest this information: it also needs to be fit for machine learning so that the prediction power of algorithms keeps improving.
This is the effort on which DRMKC embarked at the end of 2021, together with UNDRR. The interested Directorates-General of the European Commission started a working group to co-organise the existing knowledge base in a comprehensive taxonomy. Such taxonomy aims to cover not only the components of risk (hazard, exposure and vulnerability/resilience), but also DRM stages and processes (e.g. risk assessment and disaster loss data inventory).
In addition, we aim at integrating terminologies on hybrid threats, megatrends (i.e. long-term global driving forces observable in the present and likely to continue having a significant influence for a few decades) and other relevant terminologies connected to the challenges of today’s society. For example, dedicated terminologies on climate change and conflicts will also be integrated in the taxonomy.
The DRM taxonomy will also include links to EU funded projects, publications, recommendations, datasets and learning materials. It is being adopted as a reference for the ongoing developments of the Union Civil Protection Knowledge Network web platform and will be linked with work on EIOS (Epidemic Intelligence from Open Sources) and the World Health Organization Hub for Epidemic and Pandemic Intelligence.
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