A variety of research on theory and knowledge refinement that integrated knowledge engineering and machine learning was conducted in the 1990's. This work developed a variety of techniques for taking engineer knowledge in the form of propositional or first-order logical rule bases and revising them to fit empirical data using symbolic, probabilistic, and/or neural-network learning methods. We review this work to provide historical context for expanding these techniques to integrate modern knowledge engineering and machine learning methods.

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In AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering, March 2021.

@inproceedings{mooney:aaai21, title={A Recap of Early Work onTheory and Knowledge Refinement}, author={Raymond J. Mooney and Jude W. Shavlik}, booktitle={AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering}, month={March}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127884", year={2021} }

Slides (PPT)