Creating a global Climate Action Knowledge Graph to leverage artificial intelligence and speed progress on the climate agenda A positioning paper by July 2020 ​ 1 Table of Contents Summary 3 Objective 6 A connection gap, not an information gap 6 From knowledge management to knowledge discovery 9 The end of data silos 10 Why graph technology? 10 What is a knowledge graph? 11 How are knowledge graphs used today? 12 Benefits of an open knowledge graph for action on climate change 12 Accelerating learning 14 Creating a shared understanding through terminology 15 Expanding networks and increasing collaboration opportunities 15 The knowledge triangle 15 The power of explainable artificial intelligence through knowledge graphs 16 Developing the Climate Action Knowledge Graph 17 Steps to develop the Climate Action Knowledge Graph 18 Glossary 19 References 22 Authors 23 2 Summary Statement of purpose This positioning paper serves as an introduction to the often overlooked topic of how advanced data and information management practices can serve as a springboard to faster, more widespread, and more efficient climate action. It provides background on these issues for policymakers, researchers, decision makers, media and others interested in advancing climate change-related policies. The paper focuses on current challenges and readily available solutions for data and information management to further climate change adaptation, disaster risk reduction, and sustainable development agendas. It explains how a key tool - knowledge graphs - can better connect these communities and agendas to gain more traction in practice. It outlines how this single tool also sets the stage for the use of artificial intelligence methodologies that hold greater promise for innovation and progress. The paper provides real-world examples of the use of knowledge graphs in practice. The positioning paper proposes and argues for the development of a global Climate Action Knowledge Graph to address current barriers to the spread of needed knowledge and action. This new entity would create a comprehensive, contextualized knowledge base for the climate change adaptation, disaster risk reduction and broader climate action agendas in Europe initially, and would serve as an example of good knowledge management practice for these communities globally. It would interlink relevant but currently disconnected data, information, people, organizations and businesses, so that they can learn with and from one another. It would help all these players in the global quest to implement effective policies and measures to address major climate-related issues. The creation of a global Climate Action Knowledge Graph would enable: ● the rapid discovery of new insights and knowledge. ● the efficient exploration of important connections between domains (e.g. water, energy and agriculture) and/or stakeholders (e.g. research and industry). ● the provision of new approaches to learning. ● a foundation to set the stage to leverage powerful AI applications available now and on the technological horizon. ● a searchable, graph-based climate action knowledge management system that is easy to use, and that adds value by establishing and maintaining better links between science, policy, and climate action. 3 New advances in information technologies have a critical role to play in helping to reach global goals for climate action in line with the Paris Agreement1 and the 2030 Agenda.2 Technologies from the research frontier are also key to the success of many elements of the European Green Deal and new stimulus recovery packages proposed by the European Commission in response to the coronavirus pandemic. Specifically, investments in artificial intelligence (AI) will support aspects of European policy that are critical to achieving collective sustainable development commitments, and policy measures intended to spur economic growth, stimulate the labour market, build capacity and skills, and achieve societal resilience at all levels. As European Commission President Ursula von der Leyen has said of the recovery plan: “It turns the immense challenge we face into an opportunity…[...]...by investing in our future: the European Green Deal and digitalization will boost jobs and growth, the resilience of our societies and the health of our environment.”3 AI methods can serve as powerful tools to design and integrate responses to the world’s interlinked global missions to address climate change, reduce disaster risk, achieve sustainable development, and create healthy societies. The global aims are clear: to move “‘towards a resilient, sustainable and fair recovery path” and a “cleaner, digital and resilient future”.4 This positioning paper explores the ways in which the most cutting-edge technologies in information science can not only support but accelerate needed responses to climate change and its impacts. Such technological advancements are essential levers needed to move forward on two key issues: enhancing the use of enriched information and data in the world’s knowledge-driven economies; and creating usable, tailored, interdisciplinary and multilingual solutions for data and knowledge management systems. For example, consider ongoing efforts to align the three global agendas of climate change, disaster risk reduction and sustainable development, and to bring together all relevant stakeholders. Such integration requires analysis and evaluation of the different goals, common objectives and potential synergies (Figure 1). This can create a foundation for practical linkages in country efforts to achieve these global goals (Dazé et al., 2018). However, making these connections is complicated by the varying conceptualization and terminology used in the different communities. Measuring ambition and tracking progress achieved within each agenda in different countries and regions through the Global Stocktake on Adaptation (UNFCCC, 2015), for example, would benefit from a full description of all knowledge domains, so that they can be seamlessly linked, compared and contrasted. The current wave of AI technology will help knowledge managers and users to connect some of these dots in their data, and, what is more, to do so automatically. Applications that support this are generally based on knowledge graph technologies, which allow users to ​ 1 https://unfccc.int/files/essential_background/convention/application/pdf/english_paris_agreement.pdf ​ 2 https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustaina ble%20Development%20web.pdf 3 Europe's moment: Repair and prepare for the next generation, 27 May, 2020 press release: ​ ​ https://ec.europa.eu/commission/presscorner/detail/en/ip_20_940 4 ibid. ​ 4 map and describe complex knowledge domains and their heterogeneous data structures in an agile manner (Blumauer, 2018). This creates context and meaning across different domains and terminologies, and thereby interlinks disciplines, multiple data types, and a range of information repositories. Figure 1: Where different international policy processes connect, through direct references and through thematic linkages (Dazé et al., 2018).5 Knowledge graphs – technological tools that allow for unprecedented levels of sophistication in connecting disparate facets of knowledge – are becoming state-of-the-art in data science. What began as a Google initiative called Knowledge Graph in 2012 has since ​ ​ revolutionized data science, and, along with it, the ways in which people find the information ​ they seek – and the information they are able to find. Based on formal ontologies – in other ​ words, the explicit specifications of terms in given domains and the relationships among these terms (Gruber 1993) – a knowledge graph represents a collection of interlinked descriptions of entities. These are real-world objects, events, situations or abstract concepts. They contain large volumes of factual information and informal semantics, and make use of World Wide Web Consortium (W3C) semantic web principles,6 and thereby combine ​ 5 http://r9f.ab1.mwp.accessdomain.com/wp-content/uploads/2018/08/napgn-en-2018-alignment-to-adva nce-climate-resilient-development-overview-brief.pdf 6 https://en.wikipedia.org/wiki/Semantic_Web ​ 5 symbolic (e.g. knowledge models) and statistical (e.g. machine learning) AI methods for high-quality, comprehensive data and information management solutions. ​ ​ Knowledge graphs can seamlessly be developed and edited over time, with collaborative input and a clearly defined governance model. The initiative proposed here would thus provide a knowledge model to describe climate change action as it grows and evolves. Such a device potentially provides a needed and valuable public service. For example, those working on National Adaptation Plan (NAP) processes and national disaster risk reduction strategies, and others working in specific sectors such as water and health, could describe their own knowledge domains and define their needs, and learn about how these domains ​ link to other knowledge domains. This paper underscores the potential benefits of knowledge graphs. It offers examples of how knowledge graphs can help those working in different areas of climate change increase their collaboration, and leverage synergies between their different agendas. Objective The purpose of this concept note is to highlight the transformational potential that AI methods can have for the world of climate change research policy and practice. A cutting-edge team of leading semantic technology