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Jario.Zhang (talk | contribs) Added the currently popular parameterized knowledge representation forms and related research work. |
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Examples of knowledge representation formalisms include [[Vocabulary|vocabularies]], [[thesaurus]], [[semantic network]]s, [[Axiom system|axiom systems]], [[Frame (artificial intelligence)|frames]], [[Rule-based system|rules]], [[Logic programming|logic programs]], and [[Ontology (information science)|ontologies]]. Examples of [[automated reasoning]] engines include [[inference engine]]s, [[Automated theorem proving|theorem prover]]s, [[Boolean satisfiability problem|model generators]], and [[Statistical classification|classifiers]].
From a broader perspective, parameterized mechanisms of knowledge representation, including neural networks such as convolutional neural networks and transformers—can also be regarded as a type of knowledge representation formalisms. In AI systems, determining which form of knowledge representation is most suitable has long been a widely debated topic. For example, van Harmelen et al. discussed the suitability of logic as a formalism of knowledge representation and reviewed the anti-logicist arguments<ref>{{Cite book |last=Porter |first=Bruce |title=Handbook of knowledge representation |last2=Lifschitz |first2=Vladimir |last3=Van Harmelen |first3=Frank |date=2008 |publisher=Elsevier |isbn=978-0-444-52211-5 |edition=1st ed |series=Foundations of artificial intelligence |___location=Amsterdam Boston}}</ref>. Smolensky criticized the limitations of symbolic knowledge representation and explored the possibilities of integrating it with connectionist approaches<ref>{{Cite journal |last=Smolensky |first=Paul |date=1988-03 |title=On the proper treatment of connectionism |url=https://www.cambridge.org/core/product/identifier/S0140525X00052432/type/journal_article |journal=Behavioral and Brain Sciences |language=en |volume=11 |issue=1 |pages=1–23 |doi=10.1017/S0140525X00052432 |issn=0140-525X}}</ref>.
Interestingly, more recently, Zhang et al. have demonstrated that all universal (or equally expressive and natural) formalisms of knowledge representation are recursively isomorphic<ref>{{Cite journal |last=Zhang |first=Heng |last2=Jiang |first2=Guifei |last3=Quan |first3=Donghui |date=2025-04-11 |title=A Theory of Formalisms for Representing Knowledge |url=https://ojs.aaai.org/index.php/AAAI/article/view/33674 |journal=Proceedings of the AAAI Conference on Artificial Intelligence |language=en |volume=39 |issue=14 |pages=15257–15264 |doi=10.1609/aaai.v39i14.33674 |issn=2374-3468}}</ref>. This result suggests that mainstream knowledge representation formalisms do not differ in essence regarding the realizability of artificial general intelligence (AGI); different technical approaches can borrow from one another via recursive isomorphisms, and the core challenges will be shared.
== History ==
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