Knowledge representation in artificial intelligence employs various techniques to encode information effectively. Each technique presents distinct advantages suited to different problem domains and applications in AI.

For instance ..

Logical representation utilizes formal logic like propositional or first-order logic for precise encoding. Semantic networks organize knowledge into interconnected nodes and edges, providing a visual and relational representation. Frames offer a hierarchical structure with slots and values, facilitating the representation of objects and their properties. Rule-based systems capture domain-specific knowledge through if-then rules, often applied in expert systems. Neural networks learn and encode knowledge implicitly through patterns in data, making them adept at tasks such as pattern recognition and classification.

the methodologies and techniques used to store and manipulate knowledge within computational systems. There are several techniques of knowledge representation, each suited to different types of knowledge and problem domains.

I would suggest a searching, following the classification criteria, considering AI as a field of both symbolically represented (mostly formally represented, mainly classic Logic based), and (currently) data based (mostly Statistics, Provability theory and Optimization supported).

Otherwise and more general, deductive approaches vs inductive one.

In both cases, and in fact, we should consider a third class: mixed representations, I mean, both approaches considered (BTW, when dealing with symbolic representation, the management of knowledge uncertainty is, De Facto, a statistical approach to the knowledge representation).

If talking about concrete representations in both cases, I think Dr. Pryinka Sharma are examples of the most classical symbolic knowledge representation (I'd include ontologies).

Regarding data based, I'd first considered Artificial Neural Networks ([un]supervised), but following them there are a practically myriads of them (because any datum can be a knowledge representation in a concrete given situation).

Several decades ago I would consider symbolic and connectionist approaches, and those remarkable discussions among AI practitioners, particularly Marvin Minsky's articles.