dc.contributor.author | Balbi, S. | |
dc.contributor.author | Bagstad, K.J. | |
dc.contributor.author | Magrach, A. | |
dc.contributor.author | Sanz, M.J. | |
dc.contributor.author | Aguilar-Amuchastegui, N. | |
dc.contributor.author | Giupponi, C. | |
dc.contributor.author | Villa, F | |
dc.date.accessioned | 2023-06-23T09:46:21Z | |
dc.date.available | 2023-06-23T09:46:21Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Environmental Evidence: 11 (1): 5 (2022) | es_ES |
dc.identifier.uri | http://hdl.handle.net/10810/61605 | |
dc.description.abstract | 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. © 2022, The Author(s). | es_ES |
dc.description.sponsorship | The authors wish to thank all past and present contributors to the ARIES project. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This research is supported by the Basque Government through the BERC 2018–2021 program and by the Ikertzaile Doktoreentzako Hobekuntzarako doktoretza-ondoko Programa and by Spanish Ministry of Economy and Competitiveness MINECO through BC3 María de Maeztu excellence accreditation MDM-2017-0714. Support for KB’s time was provided by the U.S. Geological Survey Land Change Science Program. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Environmental Evidence | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/MDM-2017-0714 | es_ES |
dc.relation | EUS/BERC/BERC.2018-2021 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | * |
dc.subject | Artificial intelligence | es_ES |
dc.subject | Global challenges | es_ES |
dc.subject | Knowledge integration and synthesis | es_ES |
dc.subject | Semantics | es_ES |
dc.title | The global environmental agenda urgently needs a semantic web of knowledge | es_ES |
dc.type | info:eu-repo/semantics/annotation | es_ES |
dc.rights.holder | © 2022, The Author(s) | es_ES |
dc.rights.holder | Atribución-NoComercial-CompartirIgual 3.0 España | * |
dc.relation.publisherversion | https://dx.doi.org/10.1186/s13750-022-00258-y | es_ES |
dc.identifier.doi | 10.1186/s13750-022-00258-y | |