To facilitate interdisciplinary science, systems need to assess the knowledge base of each user and translate from the knowledge base of the data producer to that of the user to tailor the services provided (e.g., search, browse, assessment, and access). 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 extensive provenance about the sources of information, 5) users can query its contents through natural interfaces. Knowledge graphs are popular in industry, where they can improve the performance of search, home assistants, and other applications by incorporating information about people, events, products, etc. 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, and challenges that need to be overcome.