SKOS-2-HIVEGWU workshop
IntroductionsHollie White [email protected] Greenberg [email protected]
Morning Session Morning Session ScheduleSchedule
Introductions
Section 1: Characterizing Knowledge Organization Structures
Section 2: Thesauri and What They Represent
BREAK
Section 3: From Thesauri to SKOS
Section 4: From SKOS to HIVE
Exploring HIVE
Section 1: Characterizing knowledge organization
structures
Types of knowledge Types of knowledge organization structuresorganization structures
From least to most structure
Term lists
Controlled vocabularies
Thesauri
Taxonomy
Ontology
Languages for Languages for aboutnessaboutness
Indexing languages: Terminological tools
Thesauri (CV – controlled vocabulary) Subject headings lists Authority files for named entities (people, places,
structures, organizations)
Classification / Classificatory systems
Keyword lists
Natural language systems (broad interpretation)
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Term listsTerm listsControlled but semi-unstructured list
Term List in practice
http://library.lib.asu.edu/search/y
Authority filesAuthority files-standardization of names, subjects and titles for easier
identification and interoperability of information
Authority Files:
http://authorities.loc.gov/
ThesauriThesauri Less-structured and structured thesauri
Lexical semantic relationships
Composed of indexing terms/descriptors
Descriptors - representations of conceptsConcepts - Units of meaning
Thesaurus basicsThesaurus basics Preferred terms vs. non-preferred terms
--ex. dress vs. clothing
Semantic relations between terms
--broader, narrower, related
How to apply terms (guidelines, rules)
Scope notes
Common thesaural Common thesaural identifiersidentifiers
SN Scope Note Instruction, e.g. don’t invert phrases
USE Use (another term in preference to this one)
UF Used For
BT Broader Term
NT Narrower Term
RT Related Term
Controlled VocabulariesControlled Vocabularies
(less structured thesauri also referred to as subject heading lists)
Library of Congress Subject Headings (LCSH)
Sears Subject Headings
Medical Subject Headings (MeSH)
http://www.nlm.nih.gov/mesh/MBrowser.html
ThesauriThesauriThesaurus in practice
ERIC
NBII
http://thesaurus.nbii.gov/portal/server.pt
NASA thesaurus
http://www.sti.nasa.gov/thesfrm1.htm
TaxonomyTaxonomyFirst used by Carl von Linne (Linneaus) to
classify zoology.
A grouping of terms representing topics or subject categories. A taxonomy is typically structured so that its terms exhibit hierarchical relationships to one another, between broader and narrower concepts.
taxonomy == a subject-based classification that arranges the terms in the controlled vocabulary into a hierarchy (Garshol 2004)
OntologyOntology
In general (in the LIS domain): a tool to help organize knowledge a way to convey or represent a class (or classes) of things,
and relationships among the class/es.
No exact definition…this comes from the community you are coming from
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KOS used in Digital KOS used in Digital LibrariesLibraries
Looked at 269 online digital libraries and collections
KOS used:
Locally developed taxonomy (113)
LCSH (78)
Author list (34)
Thesauri (26)
Alphabetical listing (20)
Geographic arrangement (16)
Shiri, A. and Chase-Kruszewski, S. (2009) Knowledge organization systems in North American digital library collections. Program:electronic library and information systems. 43 (2) pp 121-139.
Discussion:Discussion:
Think about your own organization.
What type of controlled vocabularies, thesauri, and ontologies does your organization use for everyday work?
How do these vocabulary choices help you meet the goals of your institution?
Organizing Knowledge
Organization Structures
HodgeHodge’’s Types of Knowledge Organization Systemss Types of Knowledge Organization Systems
Terms Lists :
Authority Files, Glossaries, Gazetteers,
Dictionaries
Classifications and Categories:
Subject Headings, Classification Schemes,
Taxonomies, and Categorization Schemes
Relationship Lists:
Thesauri, Semantic Networks, OntologiesHodge, G. (2000) Systems of Knowledge Organization for Digital Libraries: Beyond Traditional Authority Files.http://www.clir.org/pubs/abstract/pub91abst.html
(McGuinness, D. L. (2003). Ontologies Come of Age. In Fensel, et al, Spinning the Semantic Web. Cambridge, MIT Press), pp. 175. [see also, p. 181 + 189])
Classical view of ILS languages
<___|____|_______|______|_____|______|______|_______|________|_____>
Simple thesauri/ deeper taxonomies low level full/intricate
Key word CV thesauri ontologies ontologies
Lists (WordNet) (OWL)
Greenberg’s Ontology Continuum
(http://jodi.tamu.edu/Articles/v04/i04/Smith/#section12)
http://www.semantic-conference.com
Section 2: Thesauri and what they represent
Examples of different types of Examples of different types of
““thesaurithesauri”” Cook’s Thesaurus
http://www.foodsubs.com/
BZZURKK! Thesaurus of Champions
http://epe.lac-bac.gc.ca/100/200/300/ktaylor/kaboom/bzzurkk.htm
General Multilingual Environmental Thesaurus
http://www.eionet.europa.eu/gemet
Common thesaural Common thesaural identifiersidentifiers
SN Scope Note Instruction, e.g. don’t invert phrases
USE Use (another term in preference to this one)
UF Used For
BT Broader Term
NT Narrower Term
RT Related Term
Syndetic Syndetic RelationshipsRelationships
Hierarchical
Equivalent
Associative
HierarchicalHierarchical Level of generality – both preferred terms
BT (broader term) Birthday cakes
BT Cakes
NT (narrower term) Cakes
NT Birthday cakes
…remember inheritance
EquivalentEquivalent When two or more terms represent the
same concept
One is the preferred term (descriptor), where all the information is collected
The other is the non-preferred and helps the user to find the appropriate term
EquivalentEquivalent
• Non-preferred term USE Preferred term– Biological diversification
USE Biodiversity
• Preferred term UF (used for) Non-preferred term– Biodiversity
UF Biological diversification
AssociativeAssociative One preferred term is related to another
preferred term
Non-hierarchical
“See also” function
In any large thesaurus, a significant number of terms will mean similar things or cover related areas, without necessarily being synonyms or fitting into a defined hierarchy
AssociativeAssociative
• Related Terms (RT) can be used to show these links within the thesaurus– Bed
RT Bedding– Paint Brushes
RT Painting– Vandalism
RT Hostility– Programming
RT Software
Exercise: Thesauri Exercise: Thesauri BuildingBuilding
• Montages
• Digital photographs
• Illustrations
• Pictures
• Photographic prints
• Drawings
• Photographs
• Daguerreotypes
• Negatives
Where to start:Where to start: Look at the overall offering Determine the aboutness Identify the “root” element or broadest term Identify groups/categories of information Start structuring based on the syndetic relations
you know Create hierarchies based on the semantic
relations Use the appropriate identifiers to show the
relationships
Section 3: From Thesauri to SKOS
Simple Knowledge Simple Knowledge Organization SystemsOrganization Systems
Classical view of ILS languages
<___|____|_______|______|_____|______|______|_______|_______|______>
Simple thesauri/ deeper taxonomies low level full/intricate
Key word CV thesauri ontologies ontologies
Lists (i.e WordNet) (i.e. OWL)
SKOS
Exam
ple
1:w
eb
Exam
ple
1:w
eb
vie
w o
f NB
II vie
w o
f NB
II entry
entry
Descriptive MarkupDescriptive Markup“the markup is used to label parts of the
document rather than to provide specific instructions as to how they should be processed. The objective is to decouple the inherent structure of the document from any particular treatment or rendition of it. Such markup is often described as "semantic".
--from Wikipedia
Markup LanguagesMarkup Languages“is a system for annotating a text in a way which is
syntactically distinguishable from that text.”
Using tags:
<tag>content to be rendered</tag>
Or a keyword in brackets to distinguish texts
--from Wikipedia
HTMLHTMLHypertext Markup Language
--language used to mark up webpages
--both descriptive and processing
HTML encodingHTML encoding<!doctype html>
<html>
<head>
<title>Hello HTML</title>
</head>
<body>
<p>Hello World!</p>
</body>
</html>
NB
II in H
TM
LN
BII in
HTM
L
<a href="#" onclick="return oamSubmitForm4178('result','result:j_id_jsp_1679715049_7:0:j_id_jsp_1679715049_9',null,[['synonym','Heterozygotes']]);">Heterozygotes</a></td><td class="valign”><table><tbody id="result:j_id_jsp_1679715049_7:0:j_id_jsp_1679715049_14:tbody_element”><tr class="odd"><td class="type">BT</td><td class="synonym"><a href="#" onclick="return oamSubmitForm4178('result','result:j_id_jsp_1679715049_7:0:j_id_jsp_1679715049_14:0:j_id_jsp_1679715049_18',null,[['synonym','Genotypes']]);">Genotypes</a></td></tr><tr class="even"><td class="type">NT</td><td class="synonym"><a href="#" onclick="return oamSubmitForm4178('result','result:j_id_jsp_1679715049_7:0:j_id_jsp_1679715049_14:1:j_id_jsp_1679715049_18',null,[['synonym','Carriers (genetics)']]);">Carriers (genetics)</a></td></tr><tr class="odd"><td class="type">RT</td><td class="synonym"><a href="#" onclick="return oamSubmitForm4178('result','result:j_id_jsp_1679715049_7:0:j_id_jsp_1679715049_14:2:j_id_jsp_1679715049_18',null,[['synonym','Heterozygosity']]);">Heterozygosity</a></td></tr><tr class="even"><td class="type">RT</td><td class="synonym"><a href="#" onclick="return oamSubmitForm4178('result','result:j_id_jsp_1679715049_7:0:j_id_jsp_1679715049_14:3:j_id_jsp_1679715049_18',null,[['synonym','Homozygotes']]);">Homozygotes</a></td></tr><tr class="odd"><td class="type">SC</td><td class="synonym">LSC Life Sciences</td></tr></tbody></table></td></tr><tr class="even"><td class="valign"><a href="#" onclick="return oamSubmitForm4178('result','result:j_id_jsp_1679715049_7:1:j_id_jsp_1679715049_9',null,[['synonym','Homozygotes']]);">Homozygotes</a></td><td class="valign”><table><tbody id="result:j_id_jsp_1679715049_7:1:j_id_jsp_1679715049_14:tbody_element”><tr class="odd"><td class="type">BT</td><td class="synonym"><a href="#" onclick="return oamSubmitForm4178('result','result:j_id_jsp_1679715049_7:1:j_id_jsp_1679715049_14:0:j_id_jsp_1679715049_18',null,[['synonym','Genotypes']]);">Genotypes</a></td></tr><tr class="even"><td class="type">RT</td><td class="synonym"><a href="#" onclick="return oamSubmitForm4178('result','result:j_id_jsp_1679715049_7:1:j_id_jsp_1679715049_14:1:j_id_jsp_1679715049_18',null,[['synonym','Heterozygotes']]);">Heterozygotes</a></td></tr><tr class="odd"><td class="type">RT</td><td class="synonym"><a href="#" onclick="return oamSubmitForm4178('result','result:j_id_jsp_1679715049_7:1:j_id_jsp_1679715049_14:2:j_id_jsp_1679715049_18',null,[['synonym','Homozygosity']]);">Homozygosity</a></td></tr><tr class="even"><td class="type">SC</td><td class="synonym">LSC Life Sciences</td></tr></tbody></table></td></tr>;
XMLXMLExtensible Markup Language
--Created by the World Wide Web Consortium (W3C).
--Used to mark up documents on the internet or electronic documents.
--Users get to describe the tags that are used and define how they are used.
XML encodingXML encoding
NB
II in X
ML
NB
II in X
ML
<CONCEPT>
<DESCRIPTOR>Zygotes</DESCRIPTOR>
<UF>Ookinetes</UF>
<BT>Ova</BT>
<NT>Oocysts</NT>
<RT>Hemizygosity</RT>
<RT>Reproduction</RT>
<RT>Zygosity</RT>
<SC>ASF Aquatic Sciences and Fisheries</SC>
<SC>LSC Life Sciences</SC>
<STA>Approved</STA>
<TYP>Descriptor</TYP>
<INP>2007-08-14</INP>
<UPD>2007-08-14</UPD>
</CONCEPT>
RDFRDFResource Description Framework
“is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata data model. It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources, using a variety of syntax formats”
--from Wikipedia
RDF data modelRDF data model is similar to Entity-Relationship or Class
diagrams,
statements about resource in subject-predicate- object expressions called “triples”.
subject = resource
predicate = traits or aspects of the resource and expresses a relationship between the subject and the object.
The sky The sky has the color has the color blueblue
RDF triple:
a subject denoting "the sky“
a predicate denoting "has the color”
an object denoting "blue”
OWLOWLWeb Ontology Language
--knowledge representation language for displaying ontologies working with logic
SKOSSKOS Family of languages used to describe thesauri,
controlled vocabulary, subject headings, and taxonomies.
NB
II in S
KO
S/R
DF
NB
II in S
KO
S/R
DF
<rdf:Description rdf:about="http://thesaurus.nbii.gov/nbii#Zygotes">
<rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/>
<skos:inScheme rdf:resource="http://thesaurus.nbii.gov/nbii#conceptScheme"/>
<skos:altLabel>Ookinetes</skos:altLabel>
<skos:broader rdf:resource="http://thesaurus.nbii.gov/nbii#Ova"/>
<skos:narrower rdf:resource="http://thesaurus.nbii.gov/nbii#Oocysts"/>
<skos:prefLabel>Zygotes</skos:prefLabel>
<skos:related rdf:resource="http://thesaurus.nbii.gov/nbii#Hemizygosity"/>
<skos:related rdf:resource="http://thesaurus.nbii.gov/nbii#Reproduction"/>
<skos:related rdf:resource="http://thesaurus.nbii.gov/nbii#Zygosity"/>
<skos:scopeNote>ASF Aquatic Sciences and Fisheries LSC Life Sciences</skos:scopeNote>
</rdf:Description>
Basic SKOS TagsBasic SKOS TagsSkos:concept
Skos:prefLabel
Skos:altLabel
Skos:broader
Skos:narrower
Skos:related
SKOS tagsSKOS tags
• SN Scope Note = skos:scopeNote
• USE Use = skos:prefLabel
• UF Used For = skos:altLabel
• BT Broader Term = skos:broader
• NT Narrower Term = skos:narrower
• RT Related Term = skos:related
Each entry term has a skos:concept
Terms vs. Concepts?Terms vs. Concepts?
Example: TableExample: Table
Lexical level : Table
Conceptual level :
What is a SKOS What is a SKOS Concept?Concept?
ZygotesBT OvaNT OocystsRT HemizygosityRT ReproductionRT ZygosityUF Ookinetes
All these relationshipsmake up a SKOS concept
Projects Using SKOS:Projects Using SKOS: Library of Congress
http://id.loc.gov/authorities/search/
Europeana
http://www.europeana.eu/portal/
HIVE
http://ils.unc.edu/mrc/hive/
EXPERIMENTING EXPERIMENTING
WITH SKOSWITH SKOSInstructions: SKOS tags can easily be mapped to identifiers found in traditional thesauri. For this activity try mapping basic SKOS tags to an TGM: Subject Terms excerpt.
Section 4: From SKOS to HIVE
OverviewOverview• HIVE—Helping Interdisciplinary Vocabulary Engineering
Motivation—Dryad repository
• HIVE—Goals, status, and design•A scenario
• Usability
• Conclusion and questions
61
HIVE modelHIVE model
<AMG> approach for integrating discipline CVs Model addressing C V cost, interoperability, and usability constraints (interdisciplinary environment)
MotivationMotivation
63
American Society of NaturalistsAmerican Naturalist
Ecological Society of AmericaEcology, Ecological Letters, Ecological Monographs, etc.
European Society for Evolutionary BiologyJournal of Evolutionary Biology
Society for Integrative and Comparative BiologyIntegrative and Comparative Biology
Society for Molecular Biology and EvolutionMolecular Biology and Evolution
Society for the Study of Evolution EvolutionSociety for Systematic Biology
Systematic BiologyCommercial journals
Molecular EcologyMolecular Phylogenetics and Evolution
Partner JournalsPartner Journals
Dryad’s workflow
~ low burden submission
<M><M>
<M>
Vocabulary needs for Vocabulary needs for DryadDryad
• Vocabulary analysis – 600 keywords, Dryad partner journals
• Vocabularies: NBII Thesaurus, LCSH, the Getty’s TGN, ERIC Thesaurus, Gene Ontology, IT IS (10 vocabularies)
• Facets: taxon, geographic name, time period, topic, research method, genotype, phenotype…
• Results431 topical terms, exact matches– NBII Thesaurus, 25%; MeSH, 18%531 terms (research method and taxon)– LCSH, 22% found exact matches, 25% partial
• Conclusion: Need multiple vocabularies
Goals, status, and Goals, status, and designdesign
HIVE...HIVE...as a solutionas a solution• Address CV (controlled vocabulary) cost, interoperability,
and usability constraints• COST: Expensive to create, maintain, and use • INTEROPERABILITY: Developed in silos (structurally
and intellectually) • USABILITY: Interface design and functionality
limitations have been well documented
HIVE Goals− Automatic metadata
generation approach that dynamically integrates discipline-specific controlled vocabularies encoded with the Simple Knowledge Organisation System (SKOS)
• Provide efficient, affordable, interoperable, and user friendly access to multiple vocabularies during metadata creation activities
• A model that can be replicated—> model and service
Three phases of HIVE:
1. Building HIVE- Vocabulary preparation- Server development
- Primate Life Histories Working Group
- Wood Anatomy and Wood Density Working Group
2. Sharing HIVE- Continuing education
(empowering information empowering information professionalsprofessionals)
3. Evaluating HIVE- Examining HIVE in Dryad
HIVE PartnersHIVE PartnersVocabulary
Partners Library of Congress: LCSH
the Getty Research Institute (GRI): TGN (Thesaurus of Geographic Names )
United States Geological Survey (USGS): NBII Thesaurus, Integrated Taxonomic Information System (ITIS)
Agrovoc Thesaurus
Advisory Board Jim Balhoff, NESCent Libby Dechman, LCSH Mike Frame, USGS Alistair Miles, Oxford, UK William Moen, University of North
Texas Eva Méndez Rodríguez, University
Carlos III of Madrid Joseph Shubitowski, Getty Research
Institute Ed Summers, LCSH Barbara Tillett, Library of Congress Kathy Wisser, Simmons Lisa Zolly, USGS
WORKSHOPS HOSTS: Columbia Univ.; Univ. of California, San Diego; Univ. of North Texas; Universidad Carlos III de Madrid, Madrid, Spain
HIVE ConstructionHIVE Construction• HIVE stores millions of concepts from different vocabularies,
and makes them available on the Web by a simple HTTP– Vocabularies are imported into HIVE using SKOS/RDF format
• HIVE is divided in two different modules:
1.HIVE Core– SKOS/RDF storage and management (SESAME/Elmo)– SMART HIVESMART HIVE: Automatic Metadata Extraction and Topic
Detection (KEA++)– Concept Retrieval (Lucene)
2.HIVE Web– Web user Interface (GWT—Google Web Toolkit)– Machine oriented interface (SOAP and REST)
A scenarioA scenario
HIVE for scientists, depositors
HIVE for information professionals: curators, professional librarians, archivists, museum catalogers
Meet AmyMeet Amy
Amy Zanne is a botanist.
Like every good scientist, she publishes.
~~~~Amy~~~~Amy
• Amy Zanne is a botanist.
• Like every good scientist, she publishes.
• She deposits data in Dryad.
Dryad’s workflow
~ low burden submission
<M><M>
<M>
UsabilityUsabilityLS and IS students (32 students) - Understanding HIVE: 3.8 on 5 pt. scale- Ease of navigation: 4.5- Concept cloud a good idea: 3.3 - Represent document accurately:
2.0 (simple HIVE), 3.3 (smart HIVE)
Advisory board (10 members)- Systems/technical folks want integration w/systems, Getty—
EAD- Librarians/KO folks, want to see term relationships- Like tag cloud, want relevance percentages- Color, placement of box, labels..
White 2009-2010; HIVE Team 2009-2010
UsabilityUsability
Huang, 2010
System usability and flow System usability and flow metricsmetrics
Huang, 2010
ChallengesChallenges Building vs. doing/analysis
• Source for HIVE generation, beyond abstracts Combining many vocabularies during the indexing/term
• matching phase is difficult, time consuming, inefficient.• NLP and machine learning offer promise
Interoperability = dumbing down • ontologies
Proof-of-concept/ illustrate the differences between HIVE and other vocabulary registries (NCBO and OBO Foundry)
General large team logistics, and having people from multiple disciplines (also the ++)
Summary and next Summary and next stepssteps
Open source, customizable, SKOS, + hybrid metadata generation
Research and evaluation Team project relating to Dryad Hollie White--dissertation Lesley Skalla--master’s paper Craig Willis– MeSH/SKOS conversion Curator interface design Workshop evaluation
User’s and developer’s groups on “Google Groups”• Long Term Ecological Research (LTER) Network (http://www.lternet.edu/)
Exploring HIVEhttp://hive.nescent.org
Questions /CommentsQuestions /CommentsHollie White
Ryan Scherle
Jane Greenberg
Craig Willis