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.

翻译：知识工程是创建和维护知识生产系统的过程。在计算机科学与人工智能发展史中，知识工程工作流被广泛应用，因为高质量知识被视为构建可靠智能代理的关键要素。然而，当前知识工程领域已发生显著变化，面临四大挑战：未满足的利益相关者需求、技术失配、新组织采用障碍，以及与软件工程实践的脱节。本文提出通过采用主流软件方法开发参考架构来应对这些挑战。通过研究不同利益相关者与时代的需求，我们识别出评估参考架构所需的23项核心质量属性，并基于这些属性对近期文献中的三个候选架构进行了评估。最后，我们探讨了构建全面参考架构的后续步骤，包括质量属性优先级排序、优势互补组件集成，以及社会技术缺失需求的支撑。由于此项工作需要协同努力，我们诚邀知识工程领域的研究者与实践者共同参与。