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 2 Smith et al NKOS May 31, 2003 Outline o 1. Viewing an example o 2. Explaining the concept model o 3. Discussing: why a strongly-structured model

Alexandria Digital Library Project 3 Smith et al NKOS May 31, 2003 Science learning spaces: Concept KOS o 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 o Organize learning materials as trajectory through concept space Lecture, lab, self-paced materials Services for creating/editing/displaying such materials

Alexandria Digital Library Project 4 Smith et al NKOS May 31, 2003 Application to learning environments o Application Introductory physical geography (F2002, S2003) o Collections created Knowledge base (KB) of strongly structured concepts Structured lectures and labs Learning objects cataloged by ADN metadata (+ concepts) o 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

Alexandria Digital Library Project 5 Smith et al NKOS May 31, 2003 Learning environment display (lecture mode) o The lecture is presented on three projection screens, showing the Concept window (left) Lecture window (center) Object window (right)

Alexandria Digital Library Project 6 Smith et al NKOS May 31, 2003 Current instructional material window o The left-hand frame displays the structure of the lecture o The right- hand frame displays the content of the lecture o 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

Alexandria Digital Library Project 7 Smith et al NKOS May 31, 2003 View of learning material by concepts

Alexandria Digital Library Project 8 Smith et al NKOS May 31, 2003

Alexandria Digital Library Project 9 Smith et al NKOS May 31, 2003 Learning environment display (lecture mode) o The lecture is presented on three projection screens, showing the Concept window (left) Lecture window (center) Object window (right)

Alexandria Digital Library Project 10 Smith et al NKOS May 31, 2003 Current instructional material window o The left-hand frame displays the structure of the lecture o The right- hand frame displays the content of the lecture o 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

Alexandria Digital Library Project 11 Smith et al NKOS May 31, 2003 Item in concept knowledge base

Alexandria Digital Library Project 12 Smith et al NKOS May 31, 2003 Outline o 1. Viewing an example o 2. Explaining the concept model o 3. Discussing: why a strongly-structured model

Alexandria Digital Library Project 13 Smith et al NKOS May 31, 2003 Model of science concepts o 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,… o Models of science concepts Already exist for chemistry (ASA), materials (NIST),… Generalize such models for this application o Structure items in concept KB using model Original design Current structure as seen from the lecture

ConceptModel Preferred ID Terms Descriptions Relationships Examples Nonpreferred HistoricalOrigins Topics FieldsOfStudy KnowledgeDomain 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 Defining Operation 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 Contains Hierarchical ScientificUse other Causes CausedBy IsPartOf HasParts

Alexandria Digital Library Project 18 Smith et al NKOS May 31, 2003 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 ModelTraditional KOS Other Semantic Tools 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) Scientific classifications Data/Graphical/Mathema tical/Computational reps PROPERTIES CAUSAL RELATIONS CO-RELATIONS APPLICATION(S) Faceted analysis Classification Codes Descriptors, entry terms Scope notes Hierarchical relations (BT/NT) Associated relations (RT/RT) Instances Concept map, semantic network Slot-instance, attribute-value Apply KOS principles to domain-specific applications

Alexandria Digital Library Project 20 Smith et al NKOS May 31, 2003 Outline o 1. Viewing an example o 2. Explaining the concept model o 3. Discussing: why a strongly-structured model

1. Term lists 2. Classification and categorization schemes 3. Relationship groups 4. Metadata content standards 5. 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

Alexandria Digital Library Project 22 Smith et al NKOS May 31, 2003 Values of these models o They support, for example, access to traditional knowledge containers, such as texts and journals, in which term-based representations of concepts occur. o They are also of value in supporting high-level graphical views (or concept maps) of the interrelationships among concepts.

Alexandria Digital Library Project 23 Smith et al NKOS May 31, 2003 Limits of these models o They could not provide deep organization of, and access to, scientific knowledge that is important for learning. o Accessing knowledge is largely restricted to the traditional information containers. o 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 Files Glossaries/Dictionaries Gazetteers Natural languageControlled language Weakly-structured Strongly-structured Classification & Categorization: Subject Headings Classification schemes Taxonomies Categorization schemes Relationship Groups : Ontologies Semantic networks Thesauri

Alexandria Digital Library Project 25 Smith et al NKOS May 31, 2003 Toward Strongly-Structured Models o These models focus on such attributes as the objective representations, operational semantics, use, and interrelationships of concepts, o all of which play important roles in constructing representations of phenomena that further understanding of MSE domains of knowledge.

Alexandria Digital Library Project 26 Smith et al NKOS May 31, 2003 Toward Strongly-Structured Models o Taxonomy + metadata (or attribute-value pairs) Ontology for knowledge based systems o Taxonomy and thesaurus + domain-specific markup languages o Specialized models for learning scientific concepts

Alexandria Digital Library Project 27 Smith et al NKOS May 31, 2003 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