SKOS-2-HIVE UNT workshop. Morning Session Schedule Introductions and Exploring HIVE Section 1: Knowledge Organization and Vocabulary Control Section 2:

SKOS-2-HIVEUNT workshop

Morning Session Morning Session ScheduleSchedule

Introductions and Exploring HIVE

Section 1: Knowledge Organization and Vocabulary Control

Section 2: From Thesauri to SKOS

BREAK

Section 3: From SKOS to HIVE

Section 4: Evaluating HIVE

IntroductionsHollie White [email protected]

Exploring HIVEhttp://hive.nescent.org

Section 1: Knowledge Organization

andVocabulary Control

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

Types of Types of Vocabulary ControlVocabulary Control

From least to most structure

Term listsControlled but semi-unstructured list

Example: ASU portal-- http://library.lib.asu.edu/search/y

Controlled Vocabulary

Less structured thesauri also referred to as subject heading lists

Example: MeSH -- http://www.nlm.nih.gov/mesh/MBrowser.html

Thesauri

Composed of indexing terms/descriptors

Example: NASA -- http://www.sti.nasa.gov/thesfrm1.htm

Types of Vocabulary Types of Vocabulary Control continuedControl continued

Taxonomy

A subject-based classification that arranges the terms in the controlled vocabulary into a hierarchy (Garshol 2004)

Example: ITIS--http://www.itis.gov/

(search Abutilon menziesii)

Ontology

A way to convey or represent a class (or classes) of things, and relationships among the classes.

Example: Gene Ontology--http://www.geneontology.org/

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?

See activity page

Section 2: 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

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

Syndetic relationships are the conceptual connections between terms.

Three types of syndetic relationships 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

Identifiers to SKOS Identifiers to SKOS codecode

• 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

ConceptualizingConceptualizing

SKOSSKOSSee activity in packet

Example 1:Example 1:Web view of NBII entryWeb view of NBII entry

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

NBII in XMLNBII in XML<CONCEPT>

<DESCRIPTOR>Desert plants</DESCRIPTOR>

<BT>Desert organisms</BT>

<BT>Plants</BT>

<NT>Succulents</NT>

<SC>ORIG Original</SC>

<STA>Approved</STA>

<TYP>Descriptor</TYP>

<INP>2007-08-14</INP>

<UPD>2007-08-14</UPD>

</CONCEPT>

CreatingCreating

SKOS/XMLSKOS/XMLSee activity online

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.

http://www.w3.org/TR/rdf-concepts/

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”

Things to know Things to know about RDFabout RDF

Everything can be identified by URI’s

Resources and links can have types

Partial information is tolerated

There is no need for absolute truth

Evolution is supported

Minimalist design

http://www.w3.org/2001/12/semweb-fin/w3csw

Example of RDFExample of RDF<rdf:RDF xmlns:rdf=“http://www.w3.org/1999/02/22-rdf-

syntax-ns#”

xmlns:dc="http://purl.org/dc/elements/1.1/">

<rdf:Description rdf:about="http://hive.nescent.org/">

<dc:title>HIVE Web Interface</dc:title>

</rdf:Description>

</rdf>

NB

II in S

KO

S/R

DF

NB

II in S

KO

S/R

DF

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:skos="http://www.w3.org/2004/02/skos/core#">

<rdf:Description rdf:about="http://thesaurus.nbii.gov/nbii#Desert-plants">

<rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/>

<skos:prefLabel>Desert plants</skos:prefLabel>

<skos:broader rdf:resource="http://thesaurus.nbii.gov/nbii#Desert-organisms”/>

<skos:broader rdf:resource="http://thesaurus.nbii.gov/nbii#Plants”/>

<skos:narrower rdf:resource="http://thesaurus.nbii.gov/nbii#Succulents”/>

<skos:inScheme rdf:resource="http://thesaurus.nbii.gov/nbii#"/>

<skos:scopeNote>ORIG Original</skos:scopeNote>

</rdf:Description>

</rdf:RDF>

Deconstructing SKOS/RDF










</rdf:Description>

</rdf:RDF>










</rdf:Description>

</rdf:RDF>










</rdf:Description>

</rdf:RDF>










</rdf:Description>

</rdf:RDF>










</rdf:Description>

</rdf:RDF>










</rdf:Description>

</rdf:RDF>










</rdf:Description>

</rdf:RDF>










</rdf:Description>

</rdf:RDF>

DeconstructingDeconstructing

SKOS/RDFSKOS/RDFFor more examples of deconstruction

see packet

Constructing SKOS

See activities online

Section 3: From SKOS to HIVE

Examples of Examples of Projects/Communities Projects/Communities

Using SKOSUsing SKOS W3C’s List of SKOS/Datasets

http://www.w3.org/2001/sw/wiki/SKOS/Datasets

Library of Congress

http://id.loc.gov/authorities/search/

Europeana

http://www.europeana.eu/portal/

HIVE

http://ils.unc.edu/mrc/hive/

OverviewOverview• HIVE—Helping Interdisciplinary Vocabulary Engineering

Motivation—Dryad repository

• HIVE—Goals, status, and design•A scenario

• Usability

• Conclusion and questions

53

HIVE modelHIVE model

<AMG> approach for integrating discipline CVs Model addressing C V cost, interoperability, and usability constraints (interdisciplinary environment)

54

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

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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

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; George Washington University; 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/)

Section 4: Evaluating HIVE

Comparing manual and automatic classificationof science abstracts

Join Us @Join Us @HIVE Community

http://groups.google.com/group/hive-community

Google Code page (to get your own HIVE)

http://code.google.com/p/hive-mrc/

Questions /CommentsQuestions /CommentsHollie White

[email protected]

Ryan Scherle

[email protected]

Jane Greenberg

[email protected]

Craig Willis

[email protected]

Documents

SKOS-2-HIVE UNT workshop. Morning Session Schedule Introductions and Exploring HIVE Section 1: Knowledge Organization and Vocabulary Control Section 2: