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Representing Knowledge IMS 3801 Week 2 Lecture Information • • The content of communication; Communication - the process by which meaning is conveyed among people or between people and information storage systems. – Kennedy&Schauder, 1998 Information and Data • 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; • 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? • 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 – • Sowa, 1994 Knowledge - (human knowledge understood as ) family of classification patterns related to a specific part of a real or abstract world. – Slowin'ski, 1992 In other words, knowledge is about forming concepts What is Knowledge? • the facts, feelings or experiences known by a person or group of people (Collins dictionary) • is concerned with using information effectively (Graham and Jones, 1988) • 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 • information is raw material for production of knowledge (Alavi, 1997) • 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: • factual - undisputed relationships between items • judgemental - personal beliefs about the relationships between items • strategic - ideas about how to solve a problem Factual knowledge • • • • • 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 • • • • • 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 • • • • • 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) • • • • 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 • • "deep": • knowledge about the causal relationships between items • obtained from text books and formal training "shallow": • knowledge developed from experience • heuristics which seem to work in most situations Expert System • 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) • 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 • • • • • 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: • 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) • formal language for reasoning which cannot be represented in current machines (spatial reasoning tasks) Heuristics versus Algorithms • • • • 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) • • 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 • Approaches to Problem solving • • • • 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 – – – – – 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/