Knowledge Engineering
Knowledge-Engineering is a specialized field of Artificial-Intelligence that focuses on the design, development, and maintenance of Knowledge-Based-Systems. It involves the process of extracting knowledge from human experts or large datasets and translating it into a computational form that can be used for Automated-Reasoning. The discipline was pioneered by Edward-Feigenbaum, who argued that the intelligence of a system is derived from the quality and quantity of the knowledge it possesses rather than just its algorithms.
The core of Knowledge-Engineering lies in Knowledge-Representation. This involves using formalisms such as Description-Logics, Frames, and Production-Rules to capture facts and heuristics. A central component in modern applications is the Ontology, which provides a structured vocabulary for a specific domain. These techniques are foundational to the Semantic-Web, where standards like the Resource-Description-Framework (RDF) and the Web-Ontology-Language (OWL) allow for data interoperability across the internet. Documentation on these standards can be found through the W3C Semantic Web Wiki.
Historically, Knowledge-Engineering was synonymous with the creation of Expert-Systems like MYCIN and Dendral. Today, the field is evolving to integrate with Machine-Learning through Neuro-Symbolic-AI, which seeks to combine the interpretability of symbolic logic with the predictive power of neural networks. Detailed academic perspectives on these developments are available via the Stanford Encyclopedia of Philosophy.