peter dolog dolog [at] cs [dot] aau [dot] dk e2-201 information systems october 13, 2006
DESCRIPTION
ERP Course: Knowledge Management and Learning See articles at the course web site and in references. Peter Dolog dolog [at] cs [dot] aau [dot] dk E2-201 Information Systems October 13, 2006. Material Resources. Human Resources. Production Technology. Production Process. Finances. - PowerPoint PPT PresentationTRANSCRIPT
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ERP Course: Knowledge Management and LearningSee articles at the course web site and in references
Peter Dologdolog [at] cs [dot] aau [dot] dkE2-201Information SystemsOctober 13, 2006
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2Peter Dolog, ERP Course, KMandLearning
A Company
DATA
Material Resources
HumanResources
ProductionTechnology
Finances CustomerProduction
Process
Enquire, Evaluate, Manage, and Improve
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Knowledge Management
Knowledge Management is• A set of systematic and disciplined actions• To get a greatest value from the knowledge available
to itKnowledge in this context include
• Experience and understandings of people• IT artifacts (resources, digital objects, documents, …)
Knowledge Management involves:• Organizational, social, and managerial actions• Technology
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Organizational Learning (Nonaka)
Conversion of knowledge between tacit and explicit forms (both important for organizational effectiveness)
Tacit knowledge• known• derived from experience• embodies beliefs and values• actionable• source of innovation
Explicit knowledge• represented by an IT artifact• used in communication betweem several parties
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Conversions
Tacit to TacitSOCIALIZATION
e. g. team meetings and discussions
Tacit to ExplicitEXTERNALIZATION(conceptualization, elicitation, and articulation)
e. g. dialog within a team, answer questions
Explicit to TacitINTERNALIZATION
e. g. learn from a report
Explicit to ExplicitCOMBINATION
e. g. e-mail a report
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Tacit to Tacit
Tools which support communicationSupplementing or enhancing classical meetingsReplacing face to face meetingsGroupwaresLocating experts
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Shared experience and knowledge
Asynchronous• Listening to recorded presentation• Using news and virtual discussion forums• Tools to comment and annotate such
presentation or a jointly created documentsSynchronous
• Virtual on line meetings• Voice over IP• Video conferencing• Instant messaging
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BSCW.de
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http://kmi.open.ac.uk/
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Finding a person
Common interest to create an innovationExpert to help with a specific problemTeam creation for a specific assignmentA person for peer reviewSearch engines for peopleBased on profiles
• Explicit evidence• Extracted evidence from portfolios, projects,
and activities• Derived evidence from social interactions
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Tacit to Explicit
Creating a shared mental modelDescribing/externalizing it in an IT artefactProblem tickets in call centersTheir associations to problemsFrequent problems/questionsMetadata, conceptual models, ontologiesFormal concept analysisMatching problem descriptions with existing
solutions
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http://www.saleslogix.com/
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Explicit to Explicit
CombinationCapturing existing knowledge
• Rewarding people to make documents, problem and solution descriptions
• Use of documents, citations/references, links (page rank in google)
Multimedia analysis – speech recognition (call centers), image searching (design documents)
Search – information retrieval, digital libraries, similarity measures, exact databese query, indexing, crawling
Taxonomies, Ontologies, Metadata, Text Classification, Summarization
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Explicit to Tacit
Dealing with lost in information spaceInformation overloadUsing ontologies to visualizeHiding, summarizationAnotationsAutomatic guidanceShortcutsLocal navigation vs. global navigationTask based navigation – association with business
process or task to be supported
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Adaptive Course Structure Presentation in Interbook
Indication of your current position
Recommendation annotations (trafic light metaphor)
Visited pages
Guidance
http://www2.sis.pitt.edu/~peterb/
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Adaptive Link Annotation in Interbook
Current position and upper level content
Additional Guidance
Next items in subsequence
Outcomes and prerequisites
Learner Assesment
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Learning on-line in organizational context
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Smart (open) spaces
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Knowledge Seahttp://www2.sis.pitt.edu/%7Eir/KS/home.htm?kt_sid=470&kt_user=demo
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http://www.hcd-online.com
To make the personalization process more flexible
To satisfy a user if no results are returned
To enable ranking on results based on document analysis
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Our Approach
The adaptivity seen as decisions among variable resources where decisions are driven by information about a user
Knowledge about:• Resources with metadata seen as constraints
on use• Learner features used for comparing to the
resource metadataHorn logic oriented rules in TRIPLE used to perform
the matching which concludes with personalization information
Horn logic oriented rules in TRIPLE used to construct and rewrite user queries over metadata with restrictions based on the user profile
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eLearning Domain – Metadata Used• Lerning resource
• Concepts/Competencies as learning outcomes• Prerequisites knowledge needed for understanding a
resource• Prerequisites knowledge concepts/competencies to
understand the concepts or to gain competencies• Language used in the resource
• Learner profile• Lerner performance, competencies/concepts previously
acquired and compared to prerequisites of either resource/concept/competency
• Language/Concept preferences
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Knowledge Structure for Resources
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Knowledge Structure for Domain Concepts – the Java Tutorial
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Examples in TRIPLE: Instance of a Learning Resource kbs:'Praedikatenlogik3.pdf‚[dcq:isPartOf -> kbs:'Modul3',dcq:isRequiredBy -> kbs:'Resolution.pdf',dcq:requires -> kbs:'Praedikatenlogik2.pdf',dc:subject -> acm_ccs:'I.2.4.2.1',dc:language -> lang:de,dc:description -> 'Wie wandele ich Sätze
der Praedikatenlogik in Konjungtive Normalform um',dc:title -> 'Vorlesung Künstliche Intelligenz WS 2002 : Umwandlung in KNF
].
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Knowledge Structure For Learner Features
PerformancePortfoliosGoalsPreferencesPersonal InformationIdentificationTest Performance
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Learner Performance and Competencies
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Goals and Preferences
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Example: An Instance of a Learner Performancestudent:student1[papi:performance -> student:performance_1]. student:performance_bucket_1[papi:performance_bucket_value
-> '10min']. student:performance_bucket_1[papi:performance_bucket_name
-> 'time_on_task']. student:performance_1[papi:performance_bucket ->
student:performance_bucket_1]. student:performance_1[papi:performance_value -> '0.6']. student:performance_1[papi:performance_metric -> '0-1']. student:performance_1[papi:performance_coding ->
'number']. student:performance_1[papi:granularity -> topic]. student:performance_1[papi:learning_experience_identifier
-> kbs:'Praedikatenlogik2.pdf']. student:performance_1[papi:learning_competency ->
acm_ccs:'I.2.4.2.1']. student:performance_1[papi:issued_from_identifier ->
kbs:'Test_Praedikatenlogik2.pdf'].
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Querying Metadata in Edutella
Datalog- and RDF-Based QELCommon internal data model for QueriesSeveral wrappers implemented (file based,
relational database, concept base, ...) to support several metadata storage types
Two kinds of wrappers:• Implementing provision service• Implementing consumer (query) service
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Example: Edutella Query for Resources<rdf:RDF xmlns:RDFNsId1='http://www.edutella.org/edutella#' xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#' xmlns:rdfs='http://www.w3.org/2000/01/rdf-schema#'> <RDFNsId1:QEL3Query rdf:about='http://www.elena.org/gen#query'> <RDFNsId1:hasResultType
rdf:resource='http://www.edutella.org/edutella#TupleResult'/> <RDFNsId1:hasQueryLiteral> <RDFNsId1:RDFReifiedStatement
rdf:about='http://www.elena.org/gen#st0'> <rdf:subject
rdf:resource='http://www.elena.org/gen#Resource'
rdf:type='http://www.edutella.org/edutella#Variable' rdfs:label='Resource'/> <rdf:predicate
rdf:resource='http://purl.org/dc/elements/1.1/subject'/> <rdf:object rdf:resource='http://hoersaal.kbs.uni-
hannover.de/rdf/java_ontology.rdf#Variables'/> </RDFNsId1:RDFReifiedStatement></RDFNsId1:QEL3Query></rdf:RDF>
s(X, <dc:subject>, <java:variables>).
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Adding Restriction on Language
<rdf:Description rdf:about="&n4;genid0"> <n1:type
rdf:resource="&n4;RDFReifiedStatement"/></rdf:Description><rdf:Description rdf:about="&n4;genid0"> <n1:subject rdf:resource="&n5;Resource"/></rdf:Description><rdf:Description rdf:about="&n4;genid0"> <n1:predicate rdf:resource="&n3;language"/></rdf:Description><rdf:Description rdf:about="&n4;genid0"> <n1:object rdf:resource="&n7;de"/></rdf:Description>
s(X, <dc:subject>, <java:variables>),
s(X, <dc:language>, <lang:de>).
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A Rule for Adding Such RestrictionFORALL QUERY, VAR, PRED, OBJ, NEWLIT
QUERY[edu:hasQueryLiteral -> edu:NEWLIT] AND edu:NEWLIT[rdf:type ->
edu:RDFReifiedStatement; rdf:subject -> VAR; rdf:predicate ->
PRED; rdf:object -> OBJ]<- EXISTS LITERAL, ANY (QUERY[rdf:type ->
edu:QEL3Query;edu:hasQueryLiteral -> LITERAL] AND
LITERAL[rdf:type -> edu:RDFReifiedStatement;
rdf:subject -> VAR[rdf:type -> edu:Variable]; rdf:predicate -> dc:ANY] )
AND EXISTS A A[rdf:type ->
edu:AddSimpleRestriction; rdf:predicate -> PRED; rdf:object -> OBJ]@PP AND unify(NEWLIT, lit(VAR,PRED,OBJ)).
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36Peter Dolog, ERP Course, KMandLearning
A Rule to Generate Recomendation Annotation on ResultsFORALL U, D recommended(U, D) <- user(U) AND
document(D) AND FORALL Dl (prereq(D, Dl) -> (FORALL T (topic(Dl, T) -> (EXISTS P (U[papi:performance->P]@uli:learner
-> P[papi:learning_competency->T]@uli:learner)) AND EXISTS D (prereq(D, Dl))))).
Extending the Knowledge Structure of the Resource by at Least Additional Attribute => D[hasAnnotation->recommended].
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Recommendation in the Search Results
Mapping the value of the hasAnnotation attibute to a visual representation
hasAnnotation -> recommended => GreenBall
hasAnnotation -> not_recommended => RedBall...
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Example: A Rule for Generating Examples for Presented Learning MaterialFORALL R, E example(R,E) <- LearningResource(R) AND example(E) AND EXISTS C1 (R[dc:subject->C1]) AND FORALL C2 (R[dc:subject->C2]
-> E[dc:subject->C2]).
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The Personal Reader
Similarly to the Example Rule, Summaries, Details, Generalizations, and Excercises are generated
Mapping to Visual Representation as Separate Boxes
www.personal-reader.de
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Adaptive Hypermedia Technologies
AdaptiveHypermediaTechnologies
AdaptivePresentation
AdaptiveNavigationSupport
AdaptiveMultimediaPresentation
AdaptiveTextPresentation
Adaptation ofModality
NaturalLanguageAdaptation
CannedTextAdaptation
Inserting/RemovingFragments
Altering Fragments
Stretchtext
Sorting Fragments
Dimming Fragments
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Roles of Concepts in a Resource
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The technical approach
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Retrieving Resources
Distributed contentDistributed standard based
metadata descriptions about:• Content• Relationships
between the content• Learner
Logic Programs• Query and adapt
content delivery and its links
• Visualize adaptive navigation support
P2P
Content
Relationships
Content Metadata
Logic ProgramsLearner Model
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http://www.hcd-online.com
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Semantic Annotation
Annotator is activated from repository administration interface (for all or only new resources)
Loads (linguistically annotated) ontology/ontologies that can be used to annotate this repository
Loads textual parts (title, description, key words, content if available) of resources and stores them in full-text (Lucene) index
Finds (new) annotations• Linguistic expressions of ontology are used as queries against
full-text index to find (new) annotations• Text classification techniques are used to find additional
annotations (e.g., use combined and pruned term frequency vectors of all resources of one concept as new query)
Sends new annotations back to repository administration interface where user checks correctness
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Technological Issues
Exact Match – not always work – similarity measures considered already in Personal Reader, more doc. Analysis in HCD-Suite online
Heterogeneity (inconsistencies, contradictions)OrderingOpposite situation – not to many results/links but to
fewPerformance
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Questions?
Additional References to those at the course web site:Peter Dolog et. al: Personalization in Distributed e-
Learning Environments. In Proc. WWW2004. New York.
Peter Dolog et. al: The Personal Reader: Personalizing and Enriching Learning Resources using Semantic Web Technologies. In Proc. AH2004. Eindhoven
Peter Dolog and Wolfgang Nejdl: Semantic Web Technologies for the Adaptive Web. In Peter Brusilovsky, Alfred Kobsa, Wolfgang Nejdl (eds.), The Adaptive Web: Methods and Strategies of Web Personalization. To appear.