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

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

3. Prof Elaine Ferneley Explicit Knowledge Mend a broken leg Calculate tax Make a cake Raise an invoice Build an engine Service a boiler n nFormal and systematic: u ueasily communicated & shared in product specifications, scientific formula or as computer programs; n nManagement of explicit knowledge: u umanagement of processes and information n nAre the activities to the right information or knowledge dependent ?

4. Prof Elaine Ferneley Using Knowledge stored in Databases etc.

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

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

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

8. Prof Elaine Ferneley Some Examples nData represents a fact or statement of event without relation to other things. uEx: It is raining. nInformation embodies the understanding of a relationship of some sort, possibly cause and effect. uEx: The temperature dropped 15 degrees and then it started raining. nKnowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next. uEx: 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. nWisdom 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. uEx: 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.

9. Prof Elaine Ferneley The DIKW Pyramid

10. Prof Elaine Ferneley Definitions: Data, Information, Knowledge, Understanding and Wisdom nData is raw, it is a set of symbols, it has no meaning in itself nQuantitatively 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 nQualitatively measured by timeliness, relevance, clarity:  Can we access it when we need it  Is it what we need  Can we make sense of it nIn computing terms it can be structured as records of transactions usually stored in some sort of technology system

11. Prof Elaine Ferneley Definitions: Data, Information, Knowledge, Understanding and Wisdom nInformation is data that is processed to be useful uProvides answers to the who, what, where and when type questions ugiven 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) nInformation moves through hard and soft networks uTransform data into information by adding value in various ways

12. Prof Elaine Ferneley Definitions: Data, Information, Knowledge, Understanding and Wisdom nQuantitative information management measures e.g…. uConnectivity (no. of email accounts, Lotus notes users) uTransactions (no. of messages in a given period) nQualitative information management measures uInformativeness (did I learn something new) uUsefulness (did I benefit from the information) nIn computing terms a relational database makes information from the data stored within it

13. Prof Elaine Ferneley Definitions: Data, Information, Knowledge, Understanding and Wisdom nThe application of data and information – answers the how questions nCollection of the appropriate information with the intent of making it useful uBy memorising information you amass knowledge e.g. memorising for an exam – this is useful knowledge to pass the exam (e.g. 2*2=4) uBUT 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 nIn computing terms many applications (e.g. modelling and simulation software) exercise some type of stored knowledge

14. Prof Elaine Ferneley Definitions: Data, Information, Knowledge, Understanding and Wisdom nThe appreciation of why uThe difference between learning and memorising nIf you understand you can take existing knowledge and creating new knowledge, build upon currently held information and knowledge and develop new information and knowledge nIn 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

15. Prof Elaine Ferneley Definitions: Data, Information, Knowledge, Understanding and Wisdom nEvaluated understanding nEssence of philosophical probing uCritically questions, particularly from a human perspective of morals and ethics udiscerning what is right or wrong, good or bad nA mix of experience, values, contextual information, insight nIn computing terms may be unachievable – can a computer have a soul??

16. Prof Elaine Ferneley 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

17. Prof Elaine Ferneley Reasoning and Thinking and Generating Knowledge

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

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