Open science tools and frameworks are expanding as greater emphasis is put on interdisciplinary collaboration, and findable, accessible, interoperable, and re-usable (FAIR) data. Systems need to assess  and translate from the knowledge base of the data producer to that of the user to tailor the services provided. An open knowledge network is a knowledge graph which: 1) is publicly accessible through permissive licenses, 2) most of its contents are automatically generated, 3) is curated by many contributors, 4) has provenance about the sources of information, 5) users can query through natural interfaces. This helps users navigate aspects of open science such as preregistration, open computing, reproducibility, and data deposition. This session will discuss advancing semantic understanding across communities, how an open knowledge network could serve the needs of research community, how to construct it from existing resources, how open science has been applied, and challenges that need to be overcome.