Stored Knowledge Prof. Andrew Basden. km@basden.demon.co.uk with thanks to Prof. Elaine Ferneley

1. Stored Knowledge Prof. Andrew Basden.km@basden.demon.co.uk with thanks to Prof. Elaine Ferneley

2. From tacit to articulate knowledge “We know more than we can tell.” Michael Polanyi, 1966 MANUAL How to play soccer High Low Codifiability Articulated Tacit Prof Elaine Ferneley 2

3. Calculate tax Mend a broken leg Make a cake Raise an invoice Build an engine Service a boiler Explicit Knowledge • Formal and systematic: • easily communicated & shared in product specifications, scientific formula or as computer programs; • Management of explicit knowledge: • management of processes and information • Are the activities to the right information or knowledge dependent ? Prof Elaine Ferneley

4. Using Knowledgestored in Databases etc. Prof Elaine Ferneley

5. Rate of Motion towards Knowledge • What is this (note the point when you realise what it is but do not say) • I have a box. • The box is 3' wide, 3' deep, and 6' high. • The box is very heavy. • When you move this box you usually find lots of dirt underneath it. • Junk has a real habit of collecting on top of this box. • The box has a door on the front of it. • When you open the door the light comes on. • You usually find the box in the kitchen. • It is colder inside the box than it is outside. • There is a smaller compartment inside the box with ice in it. • When I open the box it has food in it. Prof Elaine Ferneley

6. Rate of Motion towards Knowledge • It was a refrigerator • At some point in the sequence you connected with the pattern and understood • When the pattern connected the information became knowledge to you • If presented in a different order you would still have achieved knowledge but perhaps at a different rate Prof Elaine Ferneley

7. Data: Unorganized and unprocessed facts; static; a set of discrete facts about events Information: Aggregation of data that makes decision making easier Knowledgeis derived from information in the same way information is derived from data; it is a person’s range of information Data, Information, and Knowledge Prof Elaine Ferneley

8. Some Examples • Data represents a fact or statement of event without relation to other things. • Ex: It is raining. • Information embodies the understanding of a relationship of some sort, possibly cause and effect. • Ex: The temperature dropped 15 degrees and then it started raining. • Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next. • Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains. • Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic. • Ex: It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining. Prof Elaine Ferneley

9. The DIKW Pyramid Prof Elaine Ferneley

10. Definitions:Data, Information, Knowledge, Understanding and Wisdom • Data is raw, it is a set of symbols, it has no meaning in itself • Quantitatively measured by: • How much does it cost to capture and retrieve • How quickly can it be entered and called up • How much will the system hold • Qualitatively measured by timeliness, relevance, clarity: • Can we access it when we need it • Is it what we need • Can we make sense of it • In computing terms it can be structured as records of transactions usually stored in some sort of technology system Prof Elaine Ferneley

11. Definitions: Data, Information, Knowledge, Understanding and Wisdom • Information is data that is processed to be useful • Provides answers to the who, what, where and when type questions • given a meaning through a relational connector, often regarded as a message • Sender and receiver • Changes the way the receiver perceives something – it informs them (data that makes a difference) • Receiver decides if it is information (e.g. Memo perceived as information by sender but garbage by receiver) • Information moves through hard and soft networks • Transform data into information by adding value in various ways Prof Elaine Ferneley

12. Definitions: Data, Information, Knowledge, Understanding and Wisdom • Quantitative information management measures e.g…. • Connectivity (no. of email accounts, Lotus notes users) • Transactions (no. of messages in a given period) • Qualitative information management measures • Informativeness (did I learn something new) • Usefulness (did I benefit from the information) • In computing terms a relational database makes information from the data stored within it Prof Elaine Ferneley

13. Definitions: Data, Information, Knowledge, Understanding and Wisdom • The application of data and information – answers the how questions • Collection of the appropriate information with the intent of making it useful • By memorising information you amass knowledge e.g. memorising for an exam – this is useful knowledge to pass the exam (e.g. 2*2=4) • BUT the memorising itself does not allow you to infer new knowledge (e.g.1267*342) – to solve this multiplication requires cognitive and analytical ability the is achieved at the next level – understanding • In computing terms many applications (e.g. modelling and simulation software) exercise some type of stored knowledge Prof Elaine Ferneley

14. Definitions: Data, Information, Knowledge, Understanding and Wisdom • The appreciation of why • The difference between learning and memorising • If you understand you can take existing knowledge and creating new knowledge, build upon currently held information and knowledge and develop new information and knowledge • In computing terms AI systems possess understanding in the sense that they are able to infer new information and knowledge from previously stored information and knowledge Prof Elaine Ferneley

15. Definitions: Data, Information, Knowledge, Understanding and Wisdom • Evaluated understanding • Essence of philosophical probing • Critically questions, particularly from a human perspective of morals and ethics • discerning what is right or wrong, good or bad • A mix of experience, values, contextual information, insight • In computing terms may be unachievable – can a computer have a soul?? Prof Elaine Ferneley

16. A Sequential Process of Knowing Understanding supports the transition from one stage to the next, it is not a separate level in its own right Prof Elaine Ferneley

17. ReasoningandThinkingandGenerating Knowledge Prof Elaine Ferneley

18. Reasoning by analogy: relating one concept to another Formal reasoning: using deductive or inductive methods (see next slide) Case-based reasoning: reasoning from relevant past cases Expert’s Reasoning Methods Prof Elaine Ferneley

19. Deductive reasoning: exact reasoning. It deals with exact facts and exact conclusions Inductive reasoning: reasoning from a set of facts or individual cases to a general conclusion Deductive and inductive reasoning Prof Elaine Ferneley