  Knowledge engineering is the process of creating and maintaining
knowledge-producing systems. Throughout the history of computer science and AI,
knowledge engineering workflows have been widely used because high-quality
knowledge is assumed to be crucial for reliable intelligent agents. However,
the landscape of knowledge engineering has changed, presenting four challenges:
unaddressed stakeholder requirements, mismatched technologies, adoption
barriers for new organizations, and misalignment with software engineering
practices. In this paper, we propose to address these challenges by developing
a reference architecture using a mainstream software methodology. By studying
the requirements of different stakeholders and eras, we identify 23 essential
quality attributes for evaluating reference architectures. We assess three
candidate architectures from recent literature based on these attributes.
Finally, we discuss the next steps towards a comprehensive reference
architecture, including prioritizing quality attributes, integrating components
with complementary strengths, and supporting missing socio-technical
requirements. As this endeavor requires a collaborative effort, we invite all
knowledge engineering researchers and practitioners to join us.
