Alexandria Digital Library Project Concept-based Learning Spaces Apply domain-specific KOS...
Transcript of Alexandria Digital Library Project Concept-based Learning Spaces Apply domain-specific KOS...
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Alexandria Digital Library Project
Concept-based Learning SpacesApply domain-specific KOS principles fororganizing collections/services for given
applications
Terence R. Smith,Marcia L. Zeng, and
Alexandria Digital Library (ADL) Project TeamUniversity of California, Santa Barbara
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2Smith et al NKOS May 31, 2003
Outline
t 1. Viewing an examplet 2. Explaining the concept modelt 3. Discussing: why a strongly-structured model
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Science learning spaces: Concept KOS
t Concepts of science as basic knowledge granulesSets of concepts form bases for scientific representationDL and KOS technology can support organization of sciencelearning materials in terms of concepts Collections of models of science concepts ( knowledge base ) Collections of learning objects (LO) cataloged with concepts Collections of instructional materials organized by concepts
t Organize learning materials as trajectory through
concept space Lecture, lab, self-paced materialsServices for creating/editing/displaying such materials
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Application to learning environmentst Application
Introductory physical geography (F2002, S2003)t Collections created
Knowledge base (KB) of strongly structured conceptsStructured lectures and labsLearning objects cataloged by ADN metadata (+ concepts)
t Services createdFor concepts Web-based concept input tool Graphic and text-based display tools
For instructional materials Web-based lecture composer Conceptualization graphing tool
For learning objects Metadata input tool
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Learning environment display ( lecture mode)
t The lecture is presented on three projection screens, showing the Concept window (left)Lecture window (center) Object window (right)
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Current instructional material windowt The left-hand
frame displaysthe structure of the lecture
t The right- hand frame
displays thecontent of thelecture
t ADL icons (globe image)attached to a
concept link toa display of concept
properties inthe concept window
Other icons attached to a concept link to a display of concept
examples in the illustration window
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View of learning material by concepts
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Learning environment display ( lecture mode)
t The lecture is presented on three projection screens, showing the Concept window (left)Lecture window (center) Object window (right)
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10Smith et al NKOS May 31, 2003
Current instructional material windowt The left-hand
frame displaysthe structure of the lecture
t The right- hand frame
displays thecontent of thelecture
t ADL icons (globe image)attached to a
concept link toa display of concept
properties inthe concept window
Other icons attached to a concept link to a display of concept
examples in the illustration window
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Item in concept knowledge base
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Outline
t 1. Viewing an examplet 2. Explaining the concept modelt 3. Discussing: why a strongly-structured model
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Model of science concepts
t Representing a concept involves more than termsObjective, information-rich, scientific representations e.g., for concepts of heat diffusion , DNA , drainage basin ,
Associated semantics
e.g., relating to measurement, recognition,Many interrelationships e.g., hierarchical, causative, property,
t Models of science conceptsAlready exist for chemistry (ASA), materials (NIST),
Generalize such models for this applicationt Structure items in concept KB using model
Original designCurrent structure as seen from the lecture
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Con ce ptM ode l
PreferredD
Terms
Descriptions
Relationships
Examples
Nonpreferred
Hi st ori calOri ginsTopics
FieldsOfStudyKnowl ed geD oma in
c o n c e p t u a l m o d e l - f r a m e w o r k
TypeOfConc ep t
Cl ass OfPhenomen a
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c l a s s i f i c a t i o n o f c o n c e p t s
typ e of con ce pt
abstract
methodological
syntactic (linguistic)
logical
mathematical
representation
understanding
application
observations
topics
examples
conc ret emeasurable
recognizable
interpreted abstract
identification/characterization
measures
analysis
hypotheses/evaluations
predictions/tests
statements/deviations
questions/answers
problems/solutions
applications/evaluations
facts/validations
conceptsmodels
communication
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cl ass of ph en omena
c l a s s i f i c a t i o n o f p h e n o m e n a
for m
mat eri al
event
proc ess
stat e
object
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Relationships
Co Re late d
Applications
Re pre se ntati on
Caus al
HasRepresentation
PartiallyRepresents
Methodological
PropertyPropertyOf
HasProperty
DefiningOpera tion
AbstractSyntactic
ExplicitFull
ExplicitPartial
ImplicitFull
ImplicitPartial
c o n c e p t u a l m o d e l r e l a t i o n s h i p s
SetMembership
Partitive
CotainedIn
ContainsHie rar chical
Scie nti ficUse
other
Causes
CausedBy
IsPartOf
HasParts
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Current Model of science concepts
IDTYPE and FACETCONTEXT (KNOWLEDGE DOMAIN)TERM(S) (P/NP)DESCRIPTION(S)HISTORICAL ORIGIN(S)EXAMPLE(S)HIERARCHICAL RELATIONSDEFINING OPERATIONSSCIENTIFIC REPRESENTATION(S)
Scientific classifications Data/Graphical/Mathematical/Computational repsPROPERTIESCAUSAL RELATIONSCO-RELATIONSAPPLICATION(S)
As displayedin thelecture mode
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Instances
Concept map, semanticnetwork
Slot-instance, attribute-value
Faceted analysisClassification CodesDescriptors, entry termsScope notes
Hierarchical relations (BT/NT)
Associated relations (RT/RT)Associated relations (RT/RT)Associated relations (RT/RT)
IDTYPE and FACETDOMAIN CONTEXT TERM(S) (P/NP)DESCRIPTION(S)HISTORICAL ORIGIN(S)EXAMPLE(S)HIERARCHICAL RELATIONSDEFINING OPERATIONSCONCEPTUALIZATIONSCIENTIFICREPRESENTATION(S)
Scientific classifications
Data/Graphical/Mathematical/Computational reps
PROPERTIESCAUSAL RELATIONSCO-RELATIONSAPPLICATION(S)
Other Semantic ToolsTraditional KOSADL Model
Apply KOS principles to domain-specific applications
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Outline
t 1. Viewing an examplet 2. Explaining the concept modelt 3. Discussing: why a strongly-structured model
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Term lists Classification and
categorization schemes Relationship groups Metadata content
standards General knowledge
representation languages
They typically take theform of structured setsof terms representingconcepts and theirinterrelationships.
Graphicalrepresentations ofconcepts andinterrelationshipsderived from such KOStypically take thesimple form of a set ofnamed nodes connected bynamed links.
Types of structured models
KOS
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Values of these models
t They support, for example, access to traditionalknowledge containers, such as texts and journals, inwhich term-based representations of concepts occur.
t They are also of value in supporting high-levelgraphical views (or concept maps) of theinterrelationships among concepts.
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Limits of these models
t They could not provide deep organization of, andaccess to, scientific knowledge that is important forlearning.
t Accessing knowledge is largely restricted to thetraditional information containers.
t They cannot easily support access to, or integrationof, knowledge concerning many of the attributes of concepts that make them useful in SME modelingactivities.
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A Taxonomy of KOS
Term Lists: Auth ority F ilesGlos saries/ Diction ariesGa zett eers
Natural language Controlled language
W e a k l y - s t r u c t u r e d
S t r o n g
l y - s
t r u c
t u r e
d
Classification &Categorization: Sub ject Head ing s
Clas sifica tion sch eme sTaxo no miesCa tego riza tio n sc hem es
Relationship Groups: Ont olo giesSem an tic
ne tworksTh esa uri
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Toward Strongly-Structured Models
t These models focus on such attributes as theobjective representations,operational semantics,use, andinterrelationships of concepts,
t all of which play important roles in constructingrepresentations of phenomena that furtherunderstanding of MSE domains of knowledge.
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Toward Strongly-Structured Models
t Taxonomy + metadata (or attribute-value pairs)Ontology for knowledge based systems
t Taxonomy and thesaurus + domain-specific markuplanguages
t Specialized models for learning scientific concepts
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georeferenced DL tutorials distributable software packages operational libraries: UCSB library, ... outreach; federated nodes
OPERATIONAL APPLICATIONS
gazetteers: research and community gazetteer content standard web service protocols for gazetteers,
thesauri, and other KOS ADL gazetteer thesauri for feature and object types duplicate detection for gazetteers
textual-geospatial integration services
KNOWLEDGE ORGANIZATION
distributed georeferenced DL services NSDL core infrastructure data environment (e.g., GIS) integration hardware acceleration for spatial data collaborative tools Z39.50 support ingest and workflow systems
GEOREFERENCED DIGITAL LIBRARIES
knowledgebase and lecture composing,visualization, and presentation tools
physical geography concept space andlearning object collections
applications to undergraduate education educational evaluation learning services and DL integration
digital classrooms metadata content standards learning objects computational models
EDUCATIONAL APPLICATIONS
reusable user interface components contextual maps, footprint creation KOS navigation lightweight GIS functionality
Digital Earth visualization image processing
query-by-content, classification spatial extent determination
USER INTERFACES
ADLP Activities