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Knowledge Acquisition and modelling

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1 Knowledge Acquisition and modelling
Data, information & knowledge n Data n “raw signals” n Information n meaning attached to data S O S = Save Our Souls n Knowledge n attach purpose and competence to information n potential to generate action (remember A. Newell) emergency alert ® start rescue operation A Short History of Knowledge Systems general-purpose search engines (GPS) first-generation rule-based systems (MYCIN, XCON) emergence of structured methods (early KADS) mature methodologies (CommonKADS) => from art to discipline => First generation “Expert” Systems n shallow knowledge base n single reasoning principle n uniform representation n limited explanation capabilities reasoning control knowledge operates on Introduction to Knowledge Acquisition and Elicitation

2 DIKW (Data, Information, Knowledge, Wisdom)
Pyramid Hierarchy Framework Continuum

3 Data, Information, Knowledge, Wisdom
is raw. simply exists and has no significance beyond its existence (in and of itself). It is raining Information data that has been given meaning by way of relational connection. "meaning" can be useful, but does not have to be. The temperature dropped 15 degrees and then it started raining.

4 Data, Information, Knowledge, Wisdom
the appropriate collection of information, such that it's intent is to be useful. 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. “Knowledge is a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In organizations it often becomes embedded not only in documents and repositories but also in organizational routines, processes, practices and norm”  Wallace, Danny P. (2007).Knowledge Management: Historical and Cross- Disciplinary Themes.

5 Data, Information, Knowledge, Wisdom
Understanding... Cognitive and analytical. Way you can take knowledge and synthesize new knowledge from the previously held knowledge. Wisdom... calls upon all the previous experience previous levels of consciousness upon special types of human programming (moral, ethical codes, etc.). It rains because it rains.

6 Transition

7 Example I have a box. The box is 3' wide, 3' deep, and 6' high.
The box is very heavy. The box has a door on the front of it. When I open the box it has food in it. It is colder inside the box than it is outside. You usually find the box in the kitchen. There is a smaller compartment inside the box with ice in it. When you open the door the light comes on. 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. What is it? At some point in the sequence you connected with the pattern and understood it was a description of a refrigerator. From that point on each statement only added confirmation to your understanding.

8 Types of Knowledge Procedural Conceptual How to
E.g. I Know How To Drive A Car Processes, Tasks, Activities And conditions under which tasks are performed And sequence of tasks Conceptual I know that … About ways in which things (concepts) are related to each other and their properties

9 Types of Knowledge Explicit Tacit
Knowledge at the forefront of a person’s brain Thought about in a deliberate, conscious way Concerned with basic tasks, basic relationships between concepts, basic properties of concepts Not difficult to explain Tacit Deep, embedded knowledge At the back of a person’s brain Built from experience rather than being taught Gain when practice Leads to activities which seem to require no conscious thought at all

10 Types of Knowledge How to interview an expert How to boil an egg
Basic, Explicit Knowledge Deep, Tacit Knowledge Conceptual Knowledge Procedural Knowledge How to boil an egg E=mc2 How to interview an expert The properties of knowledge The position of keys on a keyboard How to tie a shoelace How to Boil An Egg Simple task easily explained How to tie a shoelace Requires demonstration with commentary E=mc2 Simply relates concepts The position of keys on a keyboard Most people know this sub-conciously but few conciously Taken from Knowledge Acquisition in Practice A Step By Step Guide, Millton, Springer-Verlag

11 Exercise Working in groups for 10 mins
Create a version of the previous slide with examples of your own

12 Knowledge Acquisition
First need to determine what that knowledge is the process of Knowledge Acquisition and Elicitation non-trivial process The information is often locked away in the heads of people - domain experts The experts themselves may not be aware of the implicit conceptual models that they use Have to draw out and make explicit all the known knowns, unknown knowns, etc…. 12

13 Example “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know.” Donald Rumsfeld 2002 (US Secretary of Defense to 2006)

14 Knowledge Acquisition
Capturing knowledge about a subject domain From people And other sources Using this to create a store of knowledge Usable by many different applications, users and benefits Does not have to be a database Can be a knowledge web, ontology, knowledge document etc

15 Eliciting Knowledge Most knowledge is in the heads of people
People have vast amounts of knowledge People have a lot of tacit knowledge They don't know all that they know and use Tacit knowledge is hard (impossible) to describe People with knowledge in organisations are usually very busy and valuable people Each person doesn't know everything 15

16 Difficulties of knowledge acquisition
People find it difficult to Express their knowledge in a manner fully comprehensible to the person who wishes to acquire it Know exactly what the person wants Give the right level of detail Present ideas in a clear and logical order Explain all the jargon and terminology of the subject domain Recall everything relevant to the project/topic at hand Avoid drifting into talking about irrelevant things

17 Difficulties of knowledge acquisition
Person attempting to acquire knowledge from someone find it difficult to: Understand everything the person says Note down everything the person says Keep the person talking about relevant issues Maintain high level of concentration needed Check they have fully understood what has been said

18 Difficulties of Knowledge Acquisition
Arise due to human cognition and communication Humans are good at communication and performing complex activities Not good at communicating complex activities to those not from the same subject areas

19 Knowledge Acquisition Bottleneck
Nothing happens until knowledge is acquired Sources of knowledge are unreliable Domain experts provide incomplete, even incorrect knowledge Domain experts may not be able to articulate their knowledge Knowledge bases are hard to build Computational knowledge representations are complex Techniques Limited range Ignorance 19

20 Knowledge Acquisition Bottleneck
Narrow bandwidth. Available channels convert organizational knowledge from its source (either experts, documents, or transactions) are relatively narrow. Acquisition latency. Slow speed of acquisition is frequently accompanied by a delay between the time when knowledge (or the underlying data) is created and when the acquired knowledge becomes available to be shared. Knowledge inaccuracy. Experts make mistakes and so do tools used to mine data and information. Maintenance can introduce inaccuracies or inconsistencies into previously correct knowledge bases. Maintenance trap. As knowledge base grows, so does the requirement for maintenance. Previous updates that were made with insufficient care and foresight accumulate and render future maintenance more difficult . As summarised by Christian Wagner in his paper titled Breaking the Knowledge Acquisition Bottleneck Through Conversational Knowledge Management., 2006

21 Terminology - Knowledge Acquisition
A Method of Learning Aristole For our purposes Elicitation Collection Analysis Modelling Validation Of Knowledge for use in a project Process of obtaining all data, information and knowledge to get a consistent view of a person solving a problem Identifying sources, vetting for quality, combining findings …

22 Terminology - Knowledge Elicitation
Sub-set of Acquisition Focuses on retrieving knowledge from humans (usually experts) Lots of tacit

23 Terminology - Knowledge Codification
Representing knowledge in some form Model Rules Ontology Video Presentation etc

24 Terminology - Knowledge Capture
Can be used instead of Acquisition or Codification Generic term covering aspects of all three previous terms

25 Terminology – Knowledge Engineering
Feignbaum and McCorduck 1983 Integrating knowledge into a computer system To solve problems that require extensive human expertise Typically building a knowledge based system Shares a lot with software engineering Feigenbaum, Edward A.; McCorduck, Pamela (1983), The fifth generation (1st ed.), Reading, MA: Addison-Wesley

26 © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

27 Knowledge Sources Documented Undocumented Acquired from
Written, viewed, sensory, behavior Undocumented Memory Acquired from Human senses Machines © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

28 Knowledge Levels Shallow Deep Surface level Input-output
Problem solving Difficult to collect, validate Interactions betwixt system components © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

29 Knowledge Categories Declarative Procedural Metaknowledge
Descriptive representation Procedural How things work under different circumstances How to use declarative knowledge Problem solving Metaknowledge Knowledge about knowledge © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

30 Knowledge Engineers Professionals who elicit knowledge from experts
Empathetic, patient Broad range of understanding, capabilities Integrate knowledge from various sources Creates and edits code Operates tools Build knowledge base Validates information Trains users © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

31 © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

32 Typical problems addressed
Type of problem – influences out choice of the tool for building an intelligent system Something to detect faults in an electrical circuit and guide user through diagnosis Domain knowledge can often be represented as production rules and this a rule-based expert system could be the right candidate for solution Choice of tool will also depend on the form and content of the solution Systems build for diagnosis often require an explanation facility to enable them to justify their solutions This is an essential component of an expert system but not of a neural network Neural nets would be a good choice for classification and clustering problems where the result is often more important than understanding the reasoning process Next step is to identify the participants Knowledge engineer, domain expert Then specify the objectives – gain competitive edge, improve decision making, reduce labour costs 32

33 Example Algorithm - a strategy, consisting of a series of steps, guaranteed to find the solution to a problem, if there is a solution. Example: How do you find the area of a triangular board, standing up vertically with one edge on the ground? Measure the length of the edge on the ground, multiply it by the vertical height, and divide by two. The answer will be exactly right, every time. Which makes it an algorithm

34 Example Heuristic - a strategy to find the solution to a problem which is not guaranteed to work. One sort of heuristic usually gives you the right answer but sometimes gives you the wrong answer Another sort gives you an answer which isn’t 100% accurate. Example: How old are you? Subtract the year you were born in from 2012. The answer will either be exactly right, or one year short. Which makes it a heuristic.

35 Knowledge Systems Analysis and Design
Davis’ law: “For every tool there is a task perfectly suited to it”. But… It would be too optimistic to assume that for every task there is a tool perfectly suited to it. Need guidelines for selecting right tools for given problem 35

36 Knowledge Acquisition – Why a Collaborative Process ?
Knowledge engineer Domain expert Logic Try to identify global solutions, which are appropriate and can be made legitimate for all possible contexts. Aim at obtaining knowledge models which are transparent, objective, and which consider a finite number of factors. Logic Usually oriented towards the individual case of their daily working processes, e.g. the individual patients. Knowledge optimized for solutions that are appropriate for the given situation. Try to consider as many factors as possible and are tolerant against inconsistencies. KEY DIFFERENCE between knowledge-based systems and other types of software Principal difference in the attitudes and goals of domain experts and knowledge engineers: In normal software where the engineers are usually able to acquire a sufficiently deep understanding of the problem domain, so that they can build the system with little or no further assistance by domain experts. 36

37 Knowledge Acquisition – Why a Collaborative Process ?
Complex and highly specialized domains E.g. medicine Characterized by a distribution of knowledge between domain experts. Different experts – even from one and the same discipline – will have their own personal preferences and mental models. E.g. Specialists for anesthesiology will rarely presume to build knowledge models for cardiac surgery. Different perspectives improve the quality of the resulting systems, so ensure that the systems will meet the requirements from different user groups, especially from both the technical and the application domain. Domain experts must ensure that the system will be accepted and trusted by their peers. E.g will a conservative user group of medical doctors reject a clinical decision-support system which is solely designed from an engineer’s perspective? 37

38 Knowledge Acquisition – Why a Collaborative Process?
“Knowledge is commonly socially constructed, through collaborative efforts toward shared objectives or by dialogues and challenges brought about by differences in persons’ perspectives.” Gavriel Salomon, Distributed Cognitions: Psychological and Educational Considerations. Cambridge University Press, 1993 Knowledge modeling must be heavily based on communication and will usually require compromises. Models are “negotiated in a social relationship” Rammert, Relations that constitute technology And media that make a difference: Toward a social pragmatic theory, 1999 Of technicizatio negotiation is often difficult KEY POINT Experience shows that the bottleneck of building knowledge systems lies more in the social process than in the technology. 38

39 Human Cognition- Bernd Schmidt
Human cognition and scientific theory construction - iterative processes Cognition based on the construction of theoretical models exposed to experimental data from real or simulated worlds. => Human cognition is driven by feedback. Theories must be validated or updated if new observations are made. Experimental acquisition of case data is essential in many scientific disciplines choice of experiments and the construction of simulation models has an impact on the resulting theoretical models. Beneficial to take a closer look at the human cognition process, because knowledge first has to be built up in a domain expert’s mind before it is ready to be modeled. 39

40 Knowledge Acquisition – Why an Evolutionary Process?
Acquisition as a kind of theory construction Human experts have to construct formal theories about the domain Backed by knowledge either resides informally in their heads or can be acquired from some other knowledge source. Resulting knowledge model is part of a knowledge-based system which can operate in real or simulated worlds. Tests in both worlds produce feedback which allows the domain expert to revise the knowledge models. When installed in the real application scenario, the system even changes the real world and thus produces new requirements, which recursively suggest changes to the knowledge model. 40

41 Knowledge Acquisition – Why an Evolutionary Process?
We do not understand how humans carry out reasoning tasks Makes it difficult to set out a detailed specification for artefact to imitate humans Potential users are often unable to assess the benefits or usage scenarios of the new system especially when they are inexperienced computer users. Artefact modifies the work processes in which it is installed. Users modify their environment and their use of the system New working culture emerges. Changes requirements => knowledge models must be updated. Other reasons why knowledge models will almost necessarily change while the knowledge-based system is built and used. 41

42 Knowledge Acquisition – Why an Evolutionary Process?
Process cannot be completely planned Different and unknown cognitive and social perspectives. Hard to predict Often based on incorrect assumptions. Domain experts required to transparently expose their daily practice but this “practice necessarily operates with deception” Every artefact resulting is only an approximation of reality and the actors involved in the process speak different “languages”. 42

43 Knowledge Acquisition – Why an Evolutionary Process ?
Knowledge is inherently complex and vague. especially in non-deterministic domains e.g. medicine Computers require formal data structures, which can be evaluared e.g. threshold values of patient observables. Experts tend to use trial-and-error methods to determine such thresholds, until the system exposes the expected behavior. Cannot predict progress which may change beliefs in KB 43

44 Knowledge Acquisition – Why an Evolutionary Process ?
Knowledge modeling process itself produces new knowledge. Self-observation performed during analysis of the existing work processes can lead to new insights Knowledge is being translated and reorganized => evolves in the process of being encoded and formatted for the system Existing work processes are challenged when analyzed – can lead to redesign during acquisition Installation of knowledge-based systems may require “digitization” of the data flow in the process. E.g. installing a neural network, addition of a database, creation of a data warehouse 44

45 Knowledge Acquisition – Why an Evolutionary Process ?
Knowledge can not be mined and processed like a raw material, but rather comes into existence during the communication Communication will influence the resulting artefacts. Process is characterized by reciprocities between engineers and experts Information provided by the expert depends on the context. As a domain expert gets more and more used to the formal view of the knowledge engineer, he/she will adjust her style, and vice-versa. 45

46 Personal Construct Theory (George Kelly)
Theory that gives an account of how people experience the world and make sense of that experience. ‘Person as a scientist’ Emphasises human  capacity for meaning making, agency, and ongoing revision of personal systems of knowing across time Individuals are seen as creatively formulating hypotheses about the areas of their lives, in an attempt to make them understandable or predictable. Predictability is sought as a guide to practical action in concrete contexts and relationships. People engage in continuous extension, refinement, and revision of their systems of meaning Moving systems towards increased meaning Personal Construct Psychology is a way of looking at the world. The founder, George Kelly, presents a theory that gives an account of how people experience the world and make sense of that experience. 46

47 Personal Construct Theory (PCT)
Key Idea the world is 'perceived' by a person in terms of whatever 'meaning' that person applies to it and the person has the freedom to choose a different 'meaning' of whatever he or she wants. i.e. the person has the 'freedom to choose' the meaning that one prefers or likes. Alternative constructivism the person is capable of applying alternative constructions (meanings) to any events in the past, present or future.

48 PCT – Alternative Constructivism
We assume that all of our present interpretations of the universe are subject to revision or replacement... There are always some alternative constructions available to choose among in dealing with the world. => reality does not reveal itself to us directly, but can be construed in a variety of ways. Constructs are the way in which things or people are either similar or different. =>simultaneously differentiates and integrates. To construe is both to abstract from past events, and provide a reference axis for anticipating future events based on that abstraction. Kelly's notion of a personal scientist assumes that all people actively seek to predict and control events by forming relevant hypotheses, and then testing them against their experience. 48

49 PCT Within man-the-scientist model,
the individual creates his or her own ways of seeing the world in which (s)he lives; the world does not create them for him; (s)he builds constructs and tries them on for size; the constructs are sometimes organized into systems groups of constructs which embody subordinate and superordinate relationships; the same events can often be viewed in the light of two or more systems, yet the events do not belong to any system; and the individual's practical systems have particular foci and limited ranges of convenience. 49

50 PCT Assumes a contrast between individual reality, social reality and shared reality: Individuality: "persons differ from each other in their construction of events." Communality: "to the extent one person employs a construction of experience which is similar to that employed by another, his psychological processes are similar to those of the other person." Socialty: "to the extent that one person construes the construction processes of another, he may play a role in a social process involving the other person." Over the last 50 years, the theory has found its home in the areas of artificial intelligence, education, human computer interaction, and human learning. 50

51 Newell and Simon’s Human Problem Solving
Problem space A person’s internal (mental) representation of a problem, and the place where problem-solving activity takes place. Model known as performance model Represents the problem solving behavior of one person who is performing a specific task, but are not adequate for system development since they are constrained to a single performer on a single task. Seen as consisting of knowledge states, and problem solving proceeds by a selective search within the problem space, according to Newell and Simon using rules of thumb (heuristics) to guide the search. Task environment The physical and social environment in which problem solving takes place. Situations which do not influence individual behavior can be studied by only analyzing the task environment. Model known as the task model Introduced the concepts of problem space and task environment. The reason for this distinction is that individual behavior influences problem solving; this influence is greater the less structured the task is. Where behavioral aspects of problem solving are closely related to the decision maker and not to the task environment, we have to look inside the person’s mind to explain this behavior. 51

52 Newell and Simon’s Human Problem Solving
Both task and performance models are required to enable problem solving behavior to be adequately modeled within a specific domain. Unstructured environments are open for individual behavior, well-structured environments encourage common behavior. 52

53 Bias What is bias? Types of bias: All views of reality are filtered.
Bias only exists in relation to some reference point. Types of bias: Motivational bias expert makes accommodations to please the interviewer or some other audience Observational bias Limitations on our ability to accurately observe the world Cognitive bias Mistakes in use of statistics, estimation, memory, etc. Notational bias Terms used to describe a problem may affect our understanding of it 53

54 Examples Social pressure Misrepresentation Group think Anchoring
response to verbal and non-verbal cues from interviewer Group think response to reactions of other experts Impression management response to imagined reactions of managers, clients,… Wishful thinking response to hopes or possible gains Appropriation selective interpretation to support current beliefs Misrepresentation expert cannot accurately fit a response into the requested response mode Anchoring contradictory data ignored once initial solution is available Inconsistency assumptions made earlier are forgotten Availability some data are easier to recall than others Underestimation of uncertainty tendency to underestimate by a factor of 2 or 3 54

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