alexandria digital library project concept-based learning spaces apply domain-specific kos...

Alexandria Digital Library Project

Concept-based Learning Spaces

Apply domain-specific KOS principles for organizing collections/services for given

applications

Terence R. Smith, Marcia L. Zeng, and

Alexandria Digital Library (ADL) Project TeamUniversity of California, Santa Barbara


2Smith et al • NKOS • May 31, 2003

Outline

1. Viewing an example 2. Explaining the concept model 3. Discussing: why a strongly-structured model



Science learning spaces: Concept KOS

Concepts of science as basic knowledge granules Sets of concepts form bases for scientific representation DL and KOS technology can support organization of science

learning 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

Organize learning materials as “trajectory through concept space” Lecture, lab, self-paced materials Services for creating/editing/displaying such materials



Application to learning environments

Application Introductory physical geography (F2002, S2003)

Collections created Knowledge base (KB) of strongly structured concepts Structured lectures and labs Learning objects cataloged by ADN metadata (+ concepts)

Services created For 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



Learning environment display (lecture mode)

The lecture is presented on three projection screens, showing the Concept window (left) Lecture window (center) Object window (right)



Current instructional material window The left-hand

frame displays the structure of the lecture

The right-hand frame displays the content of the lecture

ADL icons (globe image) attached to a concept link to a display of concept properties in the concept window

Other icons attached to a concept link to a display of concept examples in the illustration window



View of learning material by concepts



Learning environment display (lecture mode)

The lecture is presented on three projection screens, showing the Concept window (left) Lecture window (center) Object window (right)



Current instructional material window The left-hand

frame displays the structure of the lecture

The right-hand frame displays the content of the lecture

ADL icons (globe image) attached to a concept link to a display of concept properties in the concept window

Other icons attached to a concept link to a display of concept examples in the illustration window



Item in concept knowledge base



Outline




Model of science concepts

Representing a concept involves more than terms Objective, 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,… Models of science concepts

Already exist for chemistry (ASA), materials (NIST),… Generalize such models for this application

Structure items in concept KB using model Original design Current structure as seen from the lecture

ConceptModel

PreferredID

Terms

Descriptions

Relationships

Examples

Nonpreferred

HistoricalOrigins

Topics

FieldsOfStudyKnowledgeDomain

c o n c e p t u a l m o d e l - f r a m e w o r k

TypeOfConcept

ClassOfPhenomena

c l a s s i f i c a t i o n o f c o n c e p t s

type of concept

abstract

methodological

syntactic (linguistic)

logical

mathematical

representation

understanding

application

observations

topics

examples

concrete

measurable

recognizable

interpreted abstract

identification/characterization

measures

analysis

hypotheses/evaluations

predictions/tests

statements/deviations

questions/answers

problems/solutions

applications/evaluations

facts/validations

concepts

models

communication

class of phenomena

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

form

material

event

process

state

object

… …

Relationships

CoRelated

Applications

Representation

Causal

HasRepresentation

PartiallyRepresents

Methodological

Property

PropertyOf

HasProperty

DefiningOperation

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 Contain

sHierarchical

ScientificUse

otherCauses

CausedBy

IsPartOf

HasParts



Current Model of science concepts

ID TYPE and FACET CONTEXT (KNOWLEDGE DOMAIN) TERM(S) (P/NP) DESCRIPTION(S) HISTORICAL ORIGIN(S) EXAMPLE(S) HIERARCHICAL RELATIONS DEFINING OPERATIONS SCIENTIFIC REPRESENTATION(S)

– Scientific classifications– Data/Graphical/Mathematical/Computational reps

PROPERTIES CAUSAL RELATIONS CO-RELATIONS APPLICATION(S)

As displayed in the lecture mode

ADL Model Traditional KOS Other Semantic Tools

IDTYPE and FACETDOMAIN CONTEXT TERM(S) (P/NP)DESCRIPTION(S)HISTORICAL ORIGIN(S)EXAMPLE(S)HIERARCHICAL RELATIONS DEFINING OPERATIONSCONCEPTUALIZATIONSCIENTIFIC REPRESENTATION(S)

Scientific classificationsData/Graphical/Mathematical/Computational reps

PROPERTIESCAUSAL RELATIONSCO-RELATIONSAPPLICATION(S)

Faceted analysisClassification Codes Descriptors, entry termsScope notes

Hierarchical relations (BT/NT)

Associated relations (RT/RT)Associated relations (RT/RT)Associated relations (RT/RT)

Instances

Concept map, semantic network

Slot-instance, attribute-value

Apply KOS principles to domain-specific applications



Outline


1. Term lists2. Classification and

categorization schemes3. Relationship groups4. Metadata content

standards5. General knowledge

representation languages

They typically take the form of structured sets of terms representing concepts and their interrelationships.

Graphical representations of concepts and interrelationships derived from such KOS typically take the simple form of a set of named nodes connected by named links.

Types of structured models

KOS



Values of these models

They support, for example, access to traditional knowledge containers, such as texts and journals, in which term-based representations of concepts occur.

They are also of value in supporting high-level graphical views (or “concept maps”) of the interrelationships among concepts.



Limits of these models

They could not provide deep organization of, and access to, scientific knowledge that is important for learning.

Accessing knowledge is largely restricted to the traditional information containers.

They cannot easily support access to, or integration of, knowledge concerning many of the attributes of concepts that make them useful in SME modeling activities.

A Taxonomy of KOS

Term Lists:Authority FilesGlossaries/DictionariesGazetteers

Natural language Controlled language

Wea

kly- s

truct

u red

Str o

ngly-

stru

ctur

ed

Classification &Categorization: Subject Headings

Classification schemesTaxonomiesCategorization schemes

Relationship Groups: Ontologies Semantic networksThesauri



Toward Strongly-Structured Models

These models focus on such attributes as the objective representations, operational semantics, use, and interrelationships of concepts,

all of which play important roles in constructing representations of phenomena that further understanding of MSE domains of knowledge.



Toward Strongly-Structured Models

Taxonomy + metadata (or attribute-value pairs) Ontology for knowledge based systems

Taxonomy and thesaurus + domain-specific markup languages

Specialized models for learning scientific concepts



• 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 and learning 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

alexandria digital library project concept-based learning spaces apply domain-specific kos...

Documents

concept model o

concept window

outline o

example o

concepts o services

lecture slide

model of science concepts

o models of science