knowledge representation

Artificial intelligence - Knowledge representation 1 Knowledge representation and knowledge engineering are central to AI research

Artificial intelligence - Knowledge representation 1 Among the most difficult problems in knowledge representation are:

Information science - Knowledge representation and reasoning 1 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.

Information science - Knowledge representation and reasoning 1 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

Functional decomposition - Knowledge representation 1 Processes related to functional decomposition are prevalent throughout the fields of knowledge representation and machine learning

Knowledge representation 1 Examples of knowledge representation formalisms include semantic nets, Frames, Rules, and ontologies

Knowledge representation - Overview 1 Knowledge representation makes complex software easier to define and maintain than procedural code

Knowledge representation - Overview 1 It was the failure of these efforts that led to the cognitive revolution in psychology and to the phase of AI focused on knowledge representation that resulted in expert systems, production systems, frame languages, etc.

Knowledge representation - Overview 1 Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.

Knowledge representation - Overview 1 An early example of knowledge representation is the adoption of Arabic over Roman numerals. Arabic numerals facilitate larger and more complex algebraic representations. It is an example of how finding the right formalism can enable new solutions.

Knowledge representation - Overview 1 However, FOL has two drawbacks as a knowledge representation formalism: ease of use and practicality of implementation

Knowledge representation - Overview 1 Thus, a subset of FOL can be both easier to use and more practical to implement. This was a driving motivation behind rule-based expert systems. IF-THEN rules provide a subset of FOL but a very useful one that is also very intuitive. The history of most of the early AI knowledge representation formalisms; from databases to semantic nets to theorem provers and production systems can be viewed as various design decisions on whether to emphasize expressive power or computability and efficiency.

Knowledge representation - Overview 1 In a key paper on the topic Randal Davis outlined five distinct roles to analyze a knowledge representation framework:

Knowledge representation - Overview 1 * A knowledge representation (KR) is most fundamentally a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather than acting, i.e., by reasoning about the world rather than taking action in it.

Knowledge representation - Overview 1 Knowledge representation and reasoning are a key enabling technology for the Semantic web

Knowledge representation - Overview 1 The Semantic web integrates concepts from knowledge representation and reasoning with markup languages based on XML. The Resource Description Framework (RDF) provides the basic capabilities to define knowledge-based objects on the Internet with basic features such as Is-A relations and object properties. The Web Ontology Language (OWL) adds additional semantics and integrates with automatic classification reasoners.

Knowledge representation - History 1 The earliest work in knowledge representation was focused on general problem solvers such as the General Problem Solver (GPS) system developed by Newell and Simon

Knowledge representation - History 1 Another area of knowledge representation research was the problem of common sense reasoning

Knowledge representation - History 1 Currently one of the most active areas of knowledge representation research are projects associated with the Semantic web

Knowledge representation - Characteristics 1 Ron Brachman categorizes the core issues for knowledge representation as follows:

Knowledge representation - Characteristics 1 In early systems the Lisp programming language which was modeled after the lambda calculus was often used as a form of functional knowledge representation

Knowledge representation - Characteristics 1 Meta-representation means the knowledge representation language is itself expressed in that language

Knowledge representation - Characteristics 1 All forms of knowledge representation must deal with this aspect and most do so with some variant of set theory, modeling universals as sets and subsets and definitions as elements in those sets

Knowledge representation - Characteristics 1 Efficiency was often an issue, especially for early applications of knowledge representation technology

Knowledge representation - Ontology Engineering 1 the lumped element model widely used in representing electronic circuits (e.g.,Davis R, Shrobe H E, Representing Structure and Behavior of Digital Hardware, IEEE Computer, Special Issue on Knowledge Representation, 16(10):75-82.), as well as ontologies for time, belief, and even programming itself

Semantic interoperability - Knowledge representation requirements and languages 1 A knowledge representation language may be sufficiently expressive to describe nuances of meaning in well understood fields. There are at least five levels of complexity of these.

Knowledge representation and reasoning 1 Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of Set theory|sets and subsets.

Knowledge representation and reasoning 1 Examples of knowledge representation formalisms include Semantic network|semantic nets, Frame (artificial intelligence)|Frames, Rules, and Ontology (information science)|ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers.

Knowledge representation and reasoning - History 1 A classic example of how setting an appropriate formalism leads to new solutions is the early example of the adoption of Arabic over Roman numerals. Arabic numerals facilitate larger and more complex algebraic representations, thus influencing future knowledge representation.

Knowledge representation and reasoning - History 1 Knowledge representation incorporates theories from psychology which look to understand how humans solve problems and represent knowledge

Knowledge representation and reasoning - History 1 The earliest work in computerized knowledge representation was focused on general problem solvers such as the General Problem Solver (GPS) system developed by Allen Newell and Herbert A. Simon in These systems featured data structures for planning and decomposition. The system would begin with a goal. It would then decompose that goal into sub-goals and then set out to construct strategies that could accomplish each subgoal.

Knowledge representation and reasoning - History 1 It was the failure of these efforts that led to the cognitive revolution in psychology and to the phase of AI focused on knowledge representation that resulted in expert systems in the 1970s and 80s, production systems, frame languages, etc. Rather than general problem solvers, AI changed its focus to expert systems that could match human competence on a specific task, such as medical diagnosis.

Knowledge representation and reasoning - Overview 1 In a key 1993 paper on the topic, Randall Davis of Massachusetts Institute of Technology|MIT outlined five distinct roles to analyze a knowledge representation framework:

Knowledge representation and reasoning - Characteristics 1 In 1985, Ron Brachman categorized the core issues for knowledge representation as follows:

Knowledge representation and reasoning - Characteristics 1 Meta-representation means the knowledge representation language is itself expressed in that language

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