Bradley P. Allen, University of Amsterdam, Amsterdam, The NetherlandsFilip Ilievski, Vrije Universiteit, Amsterdam, The NetherlandsAbstractKnowledge engineering is the process of creating and maintaining knowledge-producing systems.Throughout the history of computer science and AI, knowledge engineering workflows have been widelyused given the importance of high-quality knowledge for reliable intelligent agents. Meanwhile, thescope of knowledge engineering, as apparent from its target tasks and use cases, has been shifting,together with its paradigms such as expert systems, semantic web, and language modeling. Theintended use cases and supported user requirements between these paradigms have not been analyzedglobally, as new paradigms often satisfy prior pain points while possibly introducing new ones. Therecent abstraction of systemic patterns into a boxology provides an opening for aligning the requirementsand use cases of knowledge engineering with the systems, components, and software that cansatisfy them best, however, this direction has not been explored to date. This paper proposes a visionof harmonizing the best practices in the field of knowledge engineering by leveraging the softwareengineering methodology of creating reference architectures. We describe how a reference architecturecan be iteratively designed and implemented to associate user needs with recurring systemic patterns,building on top of existing knowledge engineering workflows and boxologies. We provide a six-steproadmap that can enable the development of such an architecture, consisting of scope definition, selectionof information sources, architectural analysis, synthesis of an architecture based on the informationsource analysis, evaluation through instantiation, and, ultimately, instantiation into a concretesoftware architecture. We provide an initial design and outcome of the definition of architectural scope,selection of information sources, and analysis. As the remaining steps of design, evaluation, and instantiation of the architecture are largely use-case specific, we provide a detailed description of theirprocedures and point to relevant examples. We expect that following through on this vision willlead to well-grounded reference architectures for knowledge engineering, will advance the ongoinginitiatives of organizing the neurosymbolic knowledge engineering space, and will build new linksto the software architectures and data science communities.