The global environmental agenda urgently needs a semantic web of knowledge

Environmental Evidence

Maria Jose Sanz¹  Stefano Balbi¹  Ainhoa Magrach¹  Ferdinando Villa¹  Carlo Giupponi²  Kenneth J. Bagstad³  Naikoa Aguilar-Amuchastegui⁴ 

[1] Basque Centre for Climate Change (BC3), Scientific Campus of the University of the Basque Country, Sede Building 1, 1st floor, Barrio Sarriena S/N, 48940, Leioa, Bizkaia, Spain;IKERBASQUE, Basque Foundation for Science, Plaza Euskadi, 5, 48009, Bilbao, Spain;Department of Economics, Ca’ Foscari University of Venice, Venice, Italy;U.S. Geological Survey, Geosciences and Environmental Change Science Center, Denver, CO, USA;World Wildlife Fund, Washington, DC, USA;

关键词: Global challenges;    Sustainability;    Artificial intelligence;    Semantics;    Knowledge integration and synthesis;   

DOI  :  10.1186/s13750-022-00258-y

来源: Springer

【 摘 要 】

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

【 授权许可】

CC BY