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 Team
University of California, Santa Barbara
Alexandria Digital Library Project
Outline
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1. Viewing an example
2. Explaining the concept model
3. Discussing: why a strongly-structured model
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
Science learning spaces: Concept KOS
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Concepts of science as basic knowledge granules
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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”
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Lecture, lab, self-paced materials
Services for creating/editing/displaying such materials
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
Application to learning environments
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Application
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Collections created
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Introductory physical geography (F2002, S2003)
Knowledge base (KB) of strongly structured concepts
Structured lectures and labs
Learning objects cataloged by ADN metadata (+ concepts)
Services created
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For concepts
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–
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For instructional materials
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–
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Web-based concept input tool
Graphic and text-based display tools
Web-based “lecture composer”
“Conceptualization” graphing tool
For learning objects
–
Metadata input tool
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
Learning environment display (lecture mode)

The lecture is presented on three projection screens, showing the
 Concept window (left)
 Lecture window (center)
 Object window (right)
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
Current instructional material window

The left-hand
frame displays
the structure of
the lecture

The righthand 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
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
View of learning material by concepts
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
Learning environment display (lecture mode)

The lecture is presented on three projection screens, showing the
 Concept window (left)
 Lecture window (center)
 Object window (right)
Smith et al • NKOS • May 31, 2003
9
Alexandria Digital Library Project
Current instructional material window

The left-hand
frame displays
the structure of
the lecture

The righthand 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
Smith et al • NKOS • May 31, 2003
10
Alexandria Digital Library Project
Item in concept knowledge base
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
Outline



1. Viewing an example
2. Explaining the concept model
3. Discussing: why a strongly-structured model
Smith et al • NKOS • May 31, 2003
12
Alexandria Digital Library Project
Model of science concepts
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Representing a concept involves more than terms
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Objective, information-rich, scientific representations
– e.g., for concepts of heat diffusion, DNA, drainage basin, …
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Associated semantics
– e.g., relating to measurement, recognition,…
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Many interrelationships
– e.g., hierarchical, causative, property,…
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Models of science concepts
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Already exist for chemistry (ASA), materials (NIST),…
Generalize such models for this application
Structure items in concept KB using model
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Original design
Current structure as seen from the lecture
Smith et al • NKOS • May 31, 2003
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conceptual model -framework
ID
Terms
Preferred
Nonpreferred
Descriptions
TypeOfConcept
ClassOfPhenomena
ConceptModel
KnowledgeDomain
HistoricalOrigins
Examples
Relationships
FieldsOfStudy
Topics
classification
of
concepts
type of concept
observations
abstract
syntactic (linguistic)
measures
logical
analysis
mathematical
examples
identification/
characterization
topics
representation
methodological
understanding
models
application
questions/answers
communication
problems/solutions
measurable
concrete
concepts
hypotheses/evaluations
recognizable
predictions/tests
interpreted abstract
statements/deviations
applications/evaluations
facts/validations
classification
of phenomena
class of phenomena
object
material
process
form
event
state
……
conceptual model–relationships
Relationships
CotainedIn
SetMembership
Hierarchical
ScientificUse
Partitive
ExplicitFull
HasRepresentation
ExplicitPartial
ImplicitFull
PartiallyRepresents
Defining
Operation
AbstractSyntactic
Property
HasProperty
Methodological
PropertyOf
CoRelated
CausedBy
other
HasParts
Applications
Representation
Causal
Contains
IsPartOf
Causes
ImplicitPartial
Alexandria Digital Library Project
Current Model of science concepts
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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)
As displayed
in the
lecture mode
– Scientific classifications
– Data/Graphical/Mathematical/Computational reps
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PROPERTIES
CAUSAL RELATIONS
CO-RELATIONS
APPLICATION(S)
Smith et al • NKOS • May 31, 2003
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Apply KOS principles to domain-specific applications
ADL Model
ID
TYPE and FACET
DOMAIN CONTEXT
TERM(S) (P/NP)
DESCRIPTION(S)
HISTORICAL ORIGIN(S)
EXAMPLE(S)
HIERARCHICAL RELATIONS
DEFINING OPERATIONS
CONCEPTUALIZATION
SCIENTIFIC
REPRESENTATION(S)
Traditional KOS
Faceted analysis
Classification Codes
Descriptors, entry terms
Scope notes
Instances
Hierarchical relations (BT/NT)
Concept map, semantic
network
Scientific classifications
Data/Graphical/Mathema
tical/Computational reps
PROPERTIES
CAUSAL RELATIONS
CO-RELATIONS
APPLICATION(S)
Other Semantic Tools
Slot-instance, attribute-value
Associated relations (RT/RT)
Associated relations (RT/RT)
Associated relations (RT/RT)
Alexandria Digital Library Project
Outline



1. Viewing an example
2. Explaining the concept model
3. Discussing: why a strongly-structured model
Smith et al • NKOS • May 31, 2003
20
Types of structured models
1.
2.
3.
4.
5.
Term lists
KOS
Classification and
categorization schemes
Relationship groups
Metadata content
standards
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.
Alexandria Digital Library Project
Values of these models
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
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.
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
Limits of these models
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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.
Smith et al • NKOS • May 31, 2003
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A Taxonomy of KOS
Relationship Groups:
Classification &
Categorization:
Term Lists:
Natural language
Ontologies
Semantic networks
Thesauri
Classification schemes
Taxonomies
Categorization schemes
Subject Headings
Authority Files
Glossaries/Dictionaries
Gazetteers
Controlled language
Alexandria Digital Library Project
Toward Strongly-Structured Models
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These models focus on such attributes as the
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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.
Smith et al • NKOS • May 31, 2003
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Alexandria Digital Library Project
Toward Strongly-Structured Models
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Taxonomy + metadata (or attribute-value pairs)
 Ontology for knowledge based systems
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Taxonomy and thesaurus + domain-specific markup
languages
Specialized models for learning scientific concepts
Smith et al • NKOS • May 31, 2003
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ADLP Activities
Alexandria Digital Library Project
EDUCATIONAL APPLICATIONS
• 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
USER INTERFACES
• 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
GEOREFERENCED DIGITAL LIBRARIES
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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
KNOWLEDGE ORGANIZATION
• 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
OPERATIONAL APPLICATIONS
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georeferenced DL tutorials
distributable software packages
operational libraries: UCSB library, ...
outreach; federated nodes
Smith et al • NKOS • May 31, 2003
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