Alexandria Digital Library Project Concept-based Learning Spaces Apply domain-specific KOS principles for organizing

8/14/2019 Alexandria Digital Library Project Concept-based Learning Spaces Apply domain-specific KOS principles for organizing

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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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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



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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



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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

Documents

Alexandria Digital Library Project Concept-based Learning Spaces Apply domain-specific KOS principles for organizing