Knowledge representation and reasoning: Difference between revisions

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'''Knowledge representation''' ('''KR''') aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems. Whereas '''knowledge representation''' '''and reasoning''' ('''KRR''', '''KR&R''', or '''KR²''') also aims to understand, reason and interpret knowledge. KRR is widely used in the field of [[artificial intelligence]] (AI) with the goal to represent [[information]] about the world in a form that a computer system can use to solve complex tasks, such as [[Computer-aided diagnosis|diagnosing a medical condition]] or [[natural language user interface|having a natural-language dialog]]. KR incorporates findings from psychology<ref>{{cite book |first1=Roger |last1=Schank |first2=Robert |last2=Abelson |title=Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures |date=1977 |publisher=Lawrence Erlbaum Associates, Inc.}}</ref> about how humans solve problems and represent knowledge, in order to design [[Formal system|formalisms]] that make complex systems easier to design and build. KRR also incorporates findings from [[logic]] to automate various kinds of ''reasoning''.

ExamplesTraditional KRR focuses more on the declarative representation of knowledge. Related knowledge representation formalisms mainly 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]].

In a broader sense, parameterized mechanisms of knowledge representation — including [[Neural network (machine learning)|neural network]] architectures such as convolutional [[convolutionalNeural network|neural networknetworks]]<nowiki/>s and [[Transformer (deep learning architecture)|transformertransformers]]<nowiki/>s — can also be regarded as a family of knowledge representation formalisms. The question of which formalism is most appropriate for knowledge-based systems has long been a subject of extensive debate. For instance, van Harmelen et al. discussed the suitability of logic as a knowledge representation formalism and reviewed arguments presented by anti-logicists.<ref>{{Cite book |last1=Porter |first1=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 |series=Foundations of artificial intelligence |___location=Amsterdam Boston}}</ref> Smolensky criticized the limitations of symbolic formalisms and explored the possibilities of integrating it with connectionist approaches.<ref>{{Cite journal |last=Smolensky |first=Paul |date=March 1988 |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>

More recently, Heng Zhang and his colleagues have demonstrated that all universal (or equally expressive and natural) formalisms of knowledge representation are recursively isomorphic, which indicates that mainstream knowledge representation formalisms are essentially equivalent in terms of their capacity for realizing [[artificial general intelligence]] (AGI); diverse technical approaches can draw insights from one another via recursive isomorphisms, yet the fundamental challenges are inherently shared.<ref>{{Cite journal |last1=Zhang |first1=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>