Knowledge representation

related topics
{math, number, function}
{theory, work, human}
{system, computer, user}
{@card@, make, design}
{specie, animal, plant}
{build, building, house}
{car, race, vehicle}
{county, mile, population}

Knowledge representation (KR) and reasoning is an area of artificial intelligence whose fundamental goal is to represent knowledge in a manner that facilitates inferencing (i.e. drawing conclusions) from knowledge. It analyzes how to formally think - how to use a symbol system to represent a domain of discourse (that which can be talked about), along with functions that allow inference (formalized reasoning) about the objects. Generally speaking, some kind of logic is used both to supply formal semantics of how reasoning functions apply to symbols in the domain of discourse, as well as to how to supply operators such as quantifiers, modal operators, etc. that, along with an interpretation theory, give meaning to the sentences in the logic.

When we design a knowledge representation (and a knowledge representation system to interpret sentences in the logic in order to derive inferences from them) we have to make choices across a number of design spaces. The single most important decision to be made, is the expressivity of the KR. The more expressive, the easier and more compact it is to "say something". However, more expressive languages are harder to automatically derive inferences from. An example of a less expressive KR would be propositional logic. An example of a more expressive KR would be autoepistemic temporal modal logic. Less expressive KRs may be both complete and consistent (formally less expressive than set theory). More expressive KRs may be neither complete nor consistent.

The key problem is to find a KR and a supporting reasoning system that can make the inferences your application needs within the resource constraints appropriate to the problem at hand. Recent developments in KR have been driven by the Semantic Web, and have included development of XML-based knowledge representation languages and standards, including Resource Description Framework (RDF), RDF Schema, Topic Maps, DARPA Agent Markup Language (DAML), Ontology Inference Layer (OIL), and Web Ontology Language (OWL).



Full article ▸

related documents
Multi-valued logic
Extension (semantics)
Paul Cohen (mathematician)
Modus tollens
Actual infinity
Pattern recognition
Principia Mathematica
Frequency probability
Dekker's algorithm
Principle of bivalence
Literate programming
Hilbert's fifth problem
NC (complexity)
Data integrity
Single precision
Merge algorithm
Meta-Object Facility
Linear prediction
Non-deterministic Turing machine
Data type
Symbolic logic
Symmetric tensor
Catalan's conjecture
Constant term