• knowledge representation
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Artificial intelligence - Knowledge representation
1 Knowledge representation and knowledge engineering are central to AI research
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Artificial intelligence - Knowledge representation
1 Among the most difficult problems in
knowledge representation are:
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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.https://store.theartofservice.com/the-knowledge-representation-toolkit.html
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
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Functional decomposition - Knowledge representation
1 Processes related to functional decomposition are prevalent
throughout the fields of knowledge representation and machine learning
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Knowledge representation
1 Examples of knowledge representation formalisms include semantic nets, Frames, Rules, and
ontologies
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Knowledge representation - Overview
1 Knowledge representation makes complex software easier to define and maintain than
procedural code
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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.
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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.https://store.theartofservice.com/the-knowledge-representation-toolkit.html
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.
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Knowledge representation - Overview
1 However, FOL has two drawbacks as a knowledge representation formalism: ease of use and
practicality of implementation
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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.
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Knowledge representation - Overview
1 In a key paper on the topic Randal Davis outlined five distinct roles to
analyze a knowledge representation framework:
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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.
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Knowledge representation - Overview
1 Knowledge representation and reasoning are a key enabling technology for the Semantic
web
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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.
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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
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Knowledge representation - History
1 Another area of knowledge representation research was the problem of common sense
reasoning
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Knowledge representation - History
1 Currently one of the most active areas of knowledge representation
research are projects associated with the Semantic web
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Knowledge representation - Characteristics
1 Ron Brachman categorizes the core issues for knowledge representation as follows:
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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
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Knowledge representation - Characteristics
1 Meta-representation means the knowledge representation language is itself expressed in that language
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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
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Knowledge representation - Characteristics
1 Efficiency was often an issue, especially for early applications of
knowledge representation technology
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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
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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.
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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.
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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.
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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.
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Knowledge representation and reasoning - History
1 Knowledge representation incorporates theories from
psychology which look to understand how humans solve problems and
represent knowledge
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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 1959. 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.
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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.
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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:
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Knowledge representation and reasoning - Characteristics
1 In 1985, Ron Brachman categorized the core issues for knowledge representation as
follows:
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Knowledge representation and reasoning - Characteristics
1 Meta-representation means the knowledge representation language is itself expressed in that language
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