Editing Information science (section)

=== Knowledge representation and reasoning ===
{{main|Knowledge representation and reasoning}}

Knowledge representation (KR) is an area of [[artificial intelligence]] research aimed at representing knowledge in symbols to facilitate [[inferencing]] from those [[knowledge]] elements, creating new elements of knowledge. The KR can be made to be independent of the underlying knowledge model or knowledge base system (KBS) such as a [[semantic network]].<ref name="martin" >[http://www.webkb.org/doc/papers/iccs02/iswc02.pdf "Knowledge representation in RDF/XML, KIF, Frame-CG and Formalized-English"] {{Webarchive|url=https://web.archive.org/web/20120326091831/http://www.webkb.org/doc/papers/iccs02/iswc02.pdf |date=2012-03-26 }}, Philippe Martin, Distributed System Technology Centre, QLD, Australia, July 15–19, 2002</ref>

Knowledge Representation (KR) research involves analysis of how to reason accurately and effectively and how best to use a set of symbols to represent a set of facts within a knowledge domain. A symbol vocabulary and a system of logic are combined to enable [[inference]]s about elements in the KR to create new KR sentences. Logic is used to supply formal [[semantics]] of how reasoning functions should be applied to the symbols in the KR system. Logic is also used to define how operators can process and reshape the knowledge. Examples of operators and operations include, negation, conjunction, adverbs, adjectives, quantifiers and modal operators. The logic is interpretation theory. These elements—symbols, operators, and interpretation theory—are what give sequences of symbols meaning within a KR.