Download Representing Knowledge - Information Management and Systems

Representing Knowledge
IMS 3801 Week 2 Lecture
Information
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The content of communication;
Communication - the process by which meaning is
conveyed among people or between people and
information storage systems.
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Kennedy&Schauder, 1998
Information and Data
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Data represents fact, such as raw figures (numbers) or
measurements. Data, by itself, may or may not answer
a user’s question
Example: eight people;
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Information is the refinement and use of data to
answer a specific user question
Example: We can fit eight people in the car;
Pigford&Baur, 1990
What is knowledge?
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Knowledge - (the knowledge of something) is the ability
to form a mental model that accurately represents the
thing as well as the actions that can be performed on it
and by it
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Sowa, 1994
Knowledge - (human knowledge understood as ) family
of classification patterns related to a specific part of a
real or abstract world.
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Slowin'ski, 1992
In other words, knowledge is about forming concepts
What is Knowledge?
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the facts, feelings or experiences known by a person
or group of people
(Collins dictionary)
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is concerned with using information effectively
(Graham and Jones, 1988)
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Knowledge = Facts + Beliefs + Heuristics
(Hayes-Roth, Waterman & Lenat, 1983)
(NB Heuristics is logic… it is inductive reasoning from past
experience of similar problems)
Knowledge and Intelligence
- “the ability to learn or understand from
experience; ability to acquire and retain knowledge”
– Intelligence
(Webster Dictionary, Second Edition)
– knowledge
is needed for intelligence
Information vs Knowledge
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information is raw material for production of
knowledge
(Alavi, 1997)
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flow of messages or meaning which may add to,
restructure, or change knowledge
(Muchup, 1983)
Types of Knowledge
– knowledge
may be procedural or declarative
– declarative knowledge can be represented in several
ways (rules, objects with attributes etc.)
– Artificial Intelligence programs usually separate the
declarative knowledge from the procedures to
perform the search
– individual vs social (pluralisation stage of
information continuum)
– explicit vs implicit (information) or tacit (people)
Knowledge
Knowledge may be:
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factual - undisputed relationships between items
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judgemental
- personal beliefs about the
relationships between items
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strategic
- ideas about how to solve a problem
Factual knowledge
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citrus fruit have thick skins
carnivores have forward pointing eyes and sharp
teeth
last year's cutoff score for B.Comp. (IS) was 298
runny nose is a typical symptom of a virus
when power is connected to the machine, the power
light glows
Judgemental knowledge
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large citrus fruit are unlikely to be lemons
smaller animals are likely to run more slowly as are
very large, heavy animals
students who dislike Maths are unlikely to want a
Computer Science course
a person who is pale and shivering is unwell
a funny noise in the disk drive indicates a problem
Strategic knowledge
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skin thickness distinguishes citrus fruit well (so look
at this first)
eye formation is a good indicator of the type of
animal
the expected VCE score is a good way to narrow the
range of possible courses to suggest
a cold is a more common disease than meningitis
new computer users often neglect to connect
components correctly
Forms of Knowledge
(Gammack, 1987)
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concepts - terms an expert uses to express domain
knowledge, labels for ideas
facts - relationships between concepts without any
implication of how to use them
procedures - how to carry out tasks
metaknowledge - "knowledge about knowledge"
Knowledge
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"deep":
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knowledge about the causal relationships
between items
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obtained from text books and formal training
"shallow":
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knowledge developed from experience
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heuristics which seem to work in most situations
Expert System
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a computer program that emulates the behaviour of
human experts who are solving real-world problems
associated with a particular domain of knowledge.
Pigford and Baur (1990)
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an expert system has a knowledge base and a set of
heuristics
Experts
An expert is an individual who is widely recognised as
being able to solve a particular type of problem that
most other people cannot solve nearly as efficiently or
effectively.
Harmon and King (1985)
Knowledge Based Systems
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a more general term than "Expert Systems"
there may be no expert for the problem
systems may encode policies, rules, regulations
which no one person knows completely
system may not represent any one individual's
method of problem solving
may encourage use of systems as support rather than
replacement of people
Knowledge Domains
(Kidd, 1987)
Knowledge varies in ease of representation between
domains:
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formal language exists for representation &
reasoning (Maths, geometry, programming)
•
knowledge is in form of hypotheses rather than deep
physical laws (medicine, chemistry, electronic
equipment design and diagnosis)
•
no stable, well-agreed language, no coherent
underlying theory (applications software,
management, marketing)
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formal language for reasoning which cannot be
represented in current machines (spatial reasoning
tasks)
Heuristics versus Algorithms
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1+2=3
the student's mark is the sum of all practical marks divided by the
number of assignments, plus the exam mark
black pawn at square D7 and squares D6 and D5 are empty, move pawn
from D7 to D5
this is Station St. so turn left
Heuristics versus Algorithms (2)
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exactly stated formulas which can be
logically proven
put in correct numerical data and you will
get the correct answer
Representing Knowledge
in the computer
Heuristics can be represented as IF-THEN rules.
e.g. IF (condition) AND (condition) AND ...
THEN (conclusion)
N.B. a predicate is an assertion, i.e. a statement which
you believe to be true but which is not proven
Heuristics and Logic
Heuristics are based on a formal system of logic that is
very ancient
• Formal logic has “syllogisms”
• One syllogism is
Socrates is a man
All men are mortal
Therefore Socrates is mortal
which can be restated as a rule
If Socrates is a man, and all men are mortal,
then Socrates is mortal
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Approaches to Problem solving
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express facts and judgement as rules in a network
search for a path through the rule network which
gives an answer
strategic knowledge gives clues about the best way
to search
avoid exhaustive searching
The Basic Ideas
of Intelligent Problem Solving
(Hayes-Roth, Waterman & Lenat, 1983)
1. Knowledge = Facts + Beliefs + Heuristics
2. Success = Finding a good-enough answer with the
resources available
3. Search efficiency directly affects success
Sources of increased problem difficulty:
a. Erroneous data or knowledge
b. Dynamically changing data
c. The number of possibilities to evaluate
d. Complex procedures for ruling out possibilities
References
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Kennedy and Schauder, (1998) Records management: a guide to corporate
record keeping. 2nd ed South Melbourne: Addison Wesley Longman Australia
Pigford, D.V. and Baur, G. (1990) Expert Systems for Business: Concepts
and Applications, Boyd and Fraser Publishing Company, Boston. chapter 3.
Yourdon, Edward (1989) Modern Structured Analysis, Prentice-Hall
International.
Alavi, Maryam (1997) Tutorial on Knowledge Management and Knowledge
Management Systems at the International Conference on Information
Systems, ICIS'97
http://www.mbs.umd.edu/is/malavi/icis-97-KMS/index.htm
Corrall, Sheila (1999) Knowledge management: Are we in the Knowledge
Management business? Ariadne, Issue 18
http://www.ariadne.ac.uk/issue18/knowledge-mgt/