knowledge representation

• knowledge representation

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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.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


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



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.



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


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.



1 However, FOL has two drawbacks as a knowledge representation formalism: ease of use and

practicality of implementation



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.



1 In a key paper on the topic Randal Davis outlined five distinct roles to

analyze a knowledge representation framework:



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.



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

web



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



1 Another area of knowledge representation research was the problem of common sense

reasoning



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:



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



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



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



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.



1 Knowledge representation incorporates theories from

psychology which look to understand how humans solve problems and

represent knowledge



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.



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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knowledge representation

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knowledge elements

knowledge engineering

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new elements of knowledge

underlying knowledge

knowledge base system

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