Knowledge Representation Languages
- (Stanford University - Jaclyn Chen)
- Overview
Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language.
A knowledge representation language is a formal language that can represent knowledge in an AI application domain. It's a medium for storing knowledge and mechanized inference.
Here are some knowledge representation languages:
- Semantic network: A network built by linguists to represent the semantic relations between words. Semantic networks are often used as a form of knowledge representation.
- Frame representation: A technology used for knowledge representation in artificial intelligence. Frames are stored as ontologies of sets and subsets of the frame concepts.
- Logical representation: The primary form of knowledge representation to AI machines with a well-defined syntax and semantics.
- Description logics (DLs): A family of knowledge representation languages that can be used to represent the terminological knowledge of an application domain.
- Web Ontology Language (OWL): A knowledge representation language for authoring ontologies. Ontologies are a formal way to describe knowledge for various domains.
- Conceptual graphs: A knowledge representation language designed as a synthesis of several different traditions.
- Natural language: One of the methods of knowledge representation. The fundamental unit of knowledge in such languages is often a sentence that consists of a set of words arranged according to grammatical rules.
Other knowledge representation languages include: production rules, syntactic inference.
[More to come ...]
