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In the book Prolog Programming for Artificial Intelligence, a large and intricate chapter (chapter 14) is dedicated to Expert Systems. In these systems, a knowledge-database is represented through facts and rules in a declarative manner, and then we use the PROLOG inference engine to derive statements and decisions.

I was wondering: are there any examples of expert systems that represent knowledge through a standard Relational Database approach and then extract facts through SQL queries? Is there any research in this area? If not, why is a rule-based approach preferred?

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A recent research example is the "Grind" system. Take a look at the paper Computing FO-Rewritings in $\mathcal{E} \mathcal{L}$ in Practice: from Atomic to Conjunctive Queries (2018) by Peter Hansen and Carsten Lutz. Here's the abstract.

A prominent approach to implementing ontology-mediated queries (OMQs) is to rewrite into a first-order query, which is then executed using a conventional SQL database system. We consider the case where the ontology is formulated in the description logic $\mathcal{E} \mathcal{L}$ and the actual query is a conjunctive query and show that rewritings of such OMQs can be efficiently computed in practice, in a sound and complete way. Our approach combines a reduction with a decomposed backwards chaining algorithm for OMQs that are based on the simpler atomic queries, also illuminating the relationship between first-order rewritings of OMQs based on conjunctive and on atomic queries. Experiments with real-world ontologies show promising results.

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