Presentation on theme: "Knowledge Acquisition and modelling"— Presentation transcript:
1 Knowledge Acquisition and modelling Data, information & knowledgen Datan “raw signals”n Informationn meaning attached to dataS O S = Save Our Soulsn Knowledgen attach purpose and competence to informationn potential to generate action (remember A. Newell)emergency alert ® start rescue operationA Short History ofKnowledge Systemsgeneral-purposesearch engines(GPS)first-generationrule-based systems(MYCIN, XCON)emergence ofstructured methods(early KADS)maturemethodologies(CommonKADS)=> from art to discipline =>First generation “Expert” Systemsn shallow knowledgebasen single reasoningprinciplen uniformrepresentationn limited explanationcapabilitiesreasoningcontrolknowledgeoperatesonIntroduction to Knowledge Acquisition and Elicitation
3 Data, Information, Knowledge, Wisdom is raw.simply exists and has no significance beyond its existence (in and of itself).It is rainingInformationdata 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 experienceprevious levels of consciousnessupon special types of human programming (moral, ethical codes, etc.).It rains because it rains.
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 CarProcesses, Tasks, ActivitiesAnd conditions under which tasks are performedAnd sequence of tasksConceptualI 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 brainThought about in a deliberate, conscious wayConcerned with basic tasks, basic relationships between concepts, basic properties of conceptsNot difficult to explainTacitDeep, embedded knowledgeAt the back of a person’s brainBuilt from experience rather than being taughtGain when practiceLeads 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 KnowledgeDeep, Tacit KnowledgeConceptual KnowledgeProcedural KnowledgeHow to boil an eggE=mc2How to interview an expertThe properties of knowledgeThe position of keys on a keyboardHow to tie a shoelaceHow to Boil An EggSimple task easily explainedHow to tie a shoelaceRequires demonstration with commentaryE=mc2Simply relates conceptsThe position of keys on a keyboardMost people know this sub-conciously but few conciouslyTaken 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 isthe process of Knowledge Acquisition and Elicitationnon-trivial processThe information is often locked away in the heads of people - domain expertsThe experts themselves may not be aware of the implicit conceptual models that they useHave 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 domainFrom peopleAnd other sourcesUsing this to create a store of knowledgeUsable by many different applications, users and benefitsDoes not have to be a databaseCan be a knowledge web, ontology, knowledge document etc
15 Eliciting Knowledge Most knowledge is in the heads of people People have vast amounts of knowledgePeople have a lot of tacit knowledgeThey don't know all that they know and useTacit knowledge is hard (impossible) to describePeople with knowledge in organisations are usually very busy and valuable peopleEach person doesn't know everything15
16 Difficulties of knowledge acquisition People find it difficult toExpress their knowledge in a manner fully comprehensible to the person who wishes to acquire itKnow exactly what the person wantsGive the right level of detailPresent ideas in a clear and logical orderExplain all the jargon and terminology of the subject domainRecall everything relevant to the project/topic at handAvoid 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 saysNote down everything the person saysKeep the person talking about relevant issuesMaintain high level of concentration neededCheck they have fully understood what has been said
18 Difficulties of Knowledge Acquisition Arise due to human cognition and communicationHumans are good at communication and performing complex activitiesNot good at communicating complex activities to those not from the same subject areas
19 Knowledge Acquisition Bottleneck Nothing happens until knowledge is acquiredSources of knowledge are unreliableDomain experts provide incomplete, even incorrect knowledgeDomain experts may not be able to articulate their knowledgeKnowledge bases are hard to buildComputational knowledge representations are complexTechniquesLimited rangeIgnorance19
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 LearningAristoleFor our purposesElicitationCollectionAnalysisModellingValidationOf Knowledge for use in a projectProcess of obtaining all data, information and knowledge to get a consistent view of a person solving a problemIdentifying sources, vetting for quality, combining findings …
22 Terminology - Knowledge Elicitation Sub-set of AcquisitionFocuses on retrieving knowledge from humans (usually experts)Lots of tacit
23 Terminology - Knowledge Codification Representing knowledge in some formModelRulesOntologyVideoPresentation etc
24 Terminology - Knowledge Capture Can be used instead of Acquisition or CodificationGeneric term covering aspects of all three previous terms
25 Terminology – Knowledge Engineering Feignbaum and McCorduck 1983Integrating knowledge into a computer systemTo solve problems that require extensive human expertiseTypically building a knowledge based systemShares a lot with software engineeringFeigenbaum, Edward A.; McCorduck, Pamela (1983), The fifth generation (1st ed.), Reading, MA: Addison-Wesley
32 Typical problems addressed Type of problem – influences out choice of the tool for building an intelligent systemSomething to detect faults in an electrical circuit and guide user through diagnosisDomain knowledge can often be represented as production rules and this a rule-based expert system could be the right candidate for solutionChoice of tool will also depend on the form and content of the solutionSystems build for diagnosis often require an explanation facility to enable them to justify their solutionsThis is an essential component of an expert system but not of a neural networkNeural nets would be a good choice for classification and clustering problems where the result is often more important than understanding the reasoning processNext step is to identify the participantsKnowledge engineer, domain expertThen specify the objectives – gain competitive edge, improve decision making, reduce labour costs32
33 ExampleAlgorithm - 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 ExampleHeuristic - 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 answerAnother 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 problem35
36 Knowledge Acquisition – Why a Collaborative Process ? Knowledge engineerDomain expertLogicTry 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.LogicUsually 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 softwarePrincipal 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 domainsE.g. medicineCharacterized 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 perspectivesimprove 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: Psychologicaland Educational Considerations. Cambridge University Press, 1993Knowledge 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, 1999Of technicizatio negotiation is often difficultKEY POINTExperience 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 processesCognitionbased on the construction of theoretical modelsexposed to experimental datafrom 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 disciplineschoice 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 constructionHuman experts have to construct formal theories about the domainBacked by knowledgeeither resides informally in their headsor 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 tasksMakes it difficult to set out a detailed specification for artefact to imitate humansPotential users are often unable to assess the benefits or usage scenarios of the new systemespecially 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 systemNew 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 plannedDifferent and unknown cognitive and social perspectives.Hard to predictOften based on incorrect assumptions.Domain experts required to transparently expose their daily practicebut 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. medicineComputers 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 KB43
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 insightsKnowledge is being translated and reorganized => evolves in the process of being encoded and formatted for the systemExisting work processes are challenged when analyzed – can lead to redesign during acquisitionInstallation 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 warehouse44
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 communicationCommunication will influence the resulting artefacts.Process is characterized by reciprocities between engineers and expertsInformation 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 timeIndividuals 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 meaningMoving systems towards increased meaningPersonal 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 Ideathe world is 'perceived' by a person in terms of whatever 'meaning' that person applies to itand 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 constructivismthe 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 systemsgroups 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; andthe individual's practical systems have particular foci and limited ranges of convenience.49
50 PCTAssumes 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 spaceA person’s internal (mental) representation of a problem, and the place where problem-solving activity takes place.Model known as performance modelRepresents 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 environmentThe 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 modelIntroduced 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 biasexpert makes accommodations to please the interviewer or some other audienceObservational biasLimitations on our ability to accurately observe the worldCognitive biasMistakes in use of statistics, estimation, memory, etc.Notational biasTerms used to describe a problem may affect our understanding of it53
54 Examples Social pressure Misrepresentation Group think Anchoring response to verbal and non-verbal cues from interviewerGroup thinkresponse to reactions of other expertsImpression managementresponse to imagined reactions of managers, clients,…Wishful thinkingresponse to hopes or possible gainsAppropriationselective interpretation to support current beliefsMisrepresentationexpert cannot accurately fit a response into the requested response modeAnchoringcontradictory data ignored once initial solution is availableInconsistencyassumptions made earlier are forgottenAvailabilitysome data are easier to recall than othersUnderestimation of uncertaintytendency to underestimate by a factor of 2 or 354