KnowledgeEngineering

Knowledge engineering is a field within artificial intelligence and information science that focuses on designing and constructing knowledge-based systems by capturing, organizing, and formalizing knowledge from experts and domain sources to support inference and decision making.

Core activities include knowledge elicitation, specification, conceptual modeling, and the formal representation of knowledge as rules, frames, ontologies, or logic. It also covers building and maintaining knowledge bases, integrating with inference engines, and validating systems through testing and domain validation.

Common representations are production rules, semantic networks, frames, and ontologies; description logics and knowledge graphs are used for formal reasoning and interoperability. A key challenge is capturing tacit knowledge and keeping knowledge accurate, complete, and up to date as domains evolve.

Knowledge engineering supports expert systems, decision support, diagnostic and configuration tools, and complex planning. It sits at the intersection of AI practice, knowledge management, and data science, providing structured domain knowledge to computational systems while requiring close collaboration with domain experts.

Historically, knowledge engineering emerged with the development of expert systems in the 1980s, emphasizing the elicitation and codification of expert knowledge for automated reasoning. Ongoing work addresses uncertainty, inconsistency, scalability, and interoperability across diverse knowledge sources.