SustainGraph: a Knowledge Graph for tracking Evolution and Interlinking of Sustainable Development Goals' Targets

The development of solutions to manage or mitigate climate change impacts is very challenging, given the complexity and dynamicity of the socio-environmental and socio-ecological systems that have to be modeled and analyzed to include qualitative variables that are not so easily quantifiable. The existence of qualitative, interoperable and well-interlinked data is considered a must to support this objective, since scientists from different disciplines will have no option but to collaborate and co-design solutions, overcoming barriers related to the semantic mis-alignment of the plethora of available data, the existence of multiple data silos that cannot be easily and jointly processed, and the lack of data quality in many of the produced datasets. In the current work, we present SustainGraph, as a Knowledge Graph that is developed to track information related to the evolution of targets defined in the United Nations Sustainable Development Goals (SDGs) at national and regional level. SustainGraph aims to act as a unified source of knowledge around information related to the SDGs, by taking advantage of the power provided by the development of graph databases and the exploitation of Machine Learning (ML) techniques for data population, knowledge production and analysis purposes. The main concepts represented in SustainGraph are detailed, while indicative usage scenarios are provided. A set of opportunities to take advantage of SustainGraph and open research areas are identified and presented.