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Artificial intelligence - Knowledge representation
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Knowledge representation and knowledge
engineering are central to AI research
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Artificial intelligence - Knowledge representation
Among the most
difficult problems in
knowledge
representation are:
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Information science - Knowledge representation and reasoning
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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.
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Information science - Knowledge representation and reasoning
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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
Processes related to functional
decomposition are prevalent
throughout the fields of knowledge
representation and machine learning
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Knowledge representation
Examples of knowledge
representation formalisms include
semantic nets, Frames, Rules, and
ontologies
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Knowledge representation - Overview
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Knowledge representation makes complex
software easier to define and maintain than
procedural code
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Knowledge representation - Overview
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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
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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.
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Knowledge representation - Overview
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
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However, FOL has two drawbacks as a
knowledge representation formalism:
ease of use and practicality of
implementation
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Knowledge representation - Overview
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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
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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
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* 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
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Knowledge representation and reasoning are a key
enabling technology for the Semantic web
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Knowledge representation - Overview
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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)
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Knowledge representation - History
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
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Another area of knowledge representation
research was the problem of common sense
reasoning
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Knowledge representation - History
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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
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Ron Brachman categorizes the core issues for
knowledge representation as follows:
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Knowledge representation - Characteristics
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
Meta-representation means the
knowledge representation language is
itself expressed in that language
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Knowledge representation - Characteristics
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
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Efficiency was often an issue, especially
for early applications of knowledge
representation technology
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Knowledge representation - Ontology Engineering
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
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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
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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
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
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
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Knowledge representation and reasoning - History
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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
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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
https://store.theartofservice.com/the-knowledge-representation-toolkit.html
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.
https://store.theartofservice.com/the-knowledge-representation-toolkit.html
Knowledge representation and reasoning - Overview
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
In 1985, Ron Brachman categorized the core
issues for knowledge representation as follows:
1
https://store.theartofservice.com/the-knowledge-representation-toolkit.html
Knowledge representation and reasoning - Characteristics
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Meta-representation means the knowledge
representation language is itself
expressed in that language
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