Research findings in ecology have the potential to drive evidence-based actions that could reverse biodiversity decline, inspire nature-based solutions to climate change and enhance restoration of severely degraded waters and lands. However, publishing findings in peer-reviewed papers alone is not sufficient to turn ecological research into action, as evidenced by the burgeoning field of translational ecology. Scholarly literature remains inaccessible to many conservation and restoration practitioners. While the open access publishing movement has increased the availability of research, the knowledge is still poorly indexed and unstructured, leading to inadequate findability. We present a solution to these challenges in the form of the Ecolink Model (ELM) – an open-source schema for creating knowledge graphs that describe environmental variables, ecological processes and the relationships between them. Drawing on core concepts from ecological modeling and advances in biomedical knowledge synthesis, we outline a model written in LinkML – a domain-agnostic data modeling language – that captures the relationships at the heart of complex systems, thereby providing a structure for knowledge graphs. ELM establishes a consistent and reusable format that enables the discovery of new connections and presents knowledge in an easily searchable, intuitive way. Knowledge graphs that are constructed using ELM have the potential to enable restoration and conservation practitioners to easily access relevant research findings, to unveil new insights using graph data science techniques and drive an AI interface to provide plain-language access to ecological knowledge as described in the graph.