Standardizing Knowledge Engineering Practices with a Reference Architecture

• URL: http://arxiv.org/abs/2404.03624v1
• Date: Thu, 4 Apr 2024 17:46:32 GMT
• Title: Standardizing Knowledge Engineering Practices with a Reference Architecture
• Authors: Bradley P. Allen, Filip Ilievski,
• Abstract summary: This paper proposes a vision of harmonizing the best practices in the field of knowledge engineering. We describe how a reference architecture can be iteratively designed and implemented to associate user needs with recurring systemic patterns.
• Score: 8.22187358555391
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 given the importance of high-quality knowledge for reliable intelligent agents. Meanwhile, the scope 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. The intended use cases and supported user requirements between these paradigms have not been analyzed globally, as new paradigms often satisfy prior pain points while possibly introducing new ones. The recent abstraction of systemic patterns into a boxology provides an opening for aligning the requirements and use cases of knowledge engineering with the systems, components, and software that can satisfy them best. This paper proposes a vision of harmonizing the best practices in the field of knowledge engineering by leveraging the software engineering methodology of creating reference architectures. We describe how a reference architecture can 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-step roadmap that can enable the development of such an architecture, providing an initial design and outcome of the definition of architectural scope, selection of information sources, and analysis. We expect that following through on this vision will lead to well-grounded reference architectures for knowledge engineering, will advance the ongoing initiatives of organizing the neurosymbolic knowledge engineering space, and will build new links to the software architectures and data science communities.

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