SKOS-2-HIVE Interactive Seminar

IntroductionsHollie White hcwhite1@email.unc.eduJane Greenberg janeg@email.unc.edu

Morning Session Morning Session ScheduleSchedule

8:30- 8:45 Introductions

8:45-9:30 Section 1: Characterizing Knowledge Organization Structures

9:30-10:15 Section 2: Thesauri and What They Represent

10:15-10:30 BREAK

10:30-11:15 Section 3: From Thesauri to SKOS

11:15-11:45 Section 4: From SKOS to HIVE

11:45-12:30 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

Hodge’s Types of Knowledge Organization SystemsHodge’s 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 Examples of different types of “thesauri”types of “thesauri”

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) Robins

BT Birds

NT (narrower term) Birds

NT Robins

…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– Nuclear Power USE Nuclear Energy– Periodicals USE Serials

• Preferred term UF (used for) Non-preferred term– Nuclear Energy UF Nuclear Power– Serials UF Periodicals

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

<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"><html><head><link type="text/css" href="http://www.nbii.gov/imageserver/plumtree/common/public/css/mainstyle19-en.css" rel="StyleSheet" lang="en"></link><title>Biocomplexity Thesaurus</title><script type="text/javascript">if (!document.getElementById('PTIncluder-js')) {document.write('<script id="PTIncluder-js" type="text/javascript" src="http://www.nbii.gov/imageserver/plumtree/common/private/js/jsincluder/LATEST/PTIncluder.js"></scr'+'ipt>');}</script><script type="text/javascript">PTIncluder.imageServerURL = 'http://www.nbii.gov/imageserver/';PTIncluder.basePath = 'plumtree/common/private/js/';PTIncluder.lang = 'en';PTIncluder.country = 'US';PTIncluder.debug = false;PTIncluder.loadComponent('jsportlet');</script><script type="text/javascript">// Define PTPortalContext for CSAPIPTPortalContext = new Object();PTPortalContext.GET_SESSION_PREFS_URL = 'http://www.nbii.gov/portal/server.pt?space=SessionPrefs&control=SessionPrefs&action=getprefs';PTPortalContext.SET_SESSION_PREFS_URL = 'http://www.nbii.gov/portal/server.pt?space=SessionPrefs&control=SessionPrefs&action=setprefs';PTPortalContext.USER_LOCALE = 'en-us';PTPortalContext.USER_LOGIN_NAME = 'Guest';

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 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

Tags vs. Concepts?Tags vs. Concepts?

2 levels:

Lexical level

Conceptual level

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

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 SKOS

Instructions: SKOS tags can easily be mapped to identifiers found in traditional thesauri. For this activity try mapping basic SKOS tags to a thesaurus 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

60

HIVE modelHIVE model

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

MotivationMotivation

62

~ Evolutionary biologists use published data more frequently than they are depositing it themselves!

~ Surveyof400 evolutionary biologist: 48 % 48 % use other data; 78% 78% had not deposited

Ecology Ecology Paleontology Paleontology Physiology Physiology Systematics Systematics Genomics Genomics Population genetics…. Population genetics….

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++ and MAUI)– Concept Retrieval (Lucene and MG4J)

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..

UsabilityUsability

System usability and flow System usability and flow metricsmetrics

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 ++)

Concluding factsConcluding facts Open source, customizable

Uses SKOS, W3C/Semantic Web enabling technology

A hybrid metadata generation process: using automatic indexing, plus author suggestions and (depending on the environment) professional metadata creators experience

User’s and developer’s groups on “Google Groups”• Long Term Ecological Research (LTER) Network

(http://www.lternet.edu/)

Future plans: integrate aspects of folksonomy into the system, explore HIVE as a front-end for access

• Vocabularies will enrich Dryad data description, and assist with access, use, reuse, etc…

• Nothing novel, but infrastructure is supportive, • HIVE is a real-world applications using Semantic

Web technology““to HIVE…”to HIVE…”

• HIVE/HIVE is applicable beyond Dryad

HIVE wiki: HIVE wiki: https://www.nescent.org/sites/hive/Main_Page

90

final…final… ConclusionsConclusions

Exploring HIVEhttp://hive.nescent.org

Questions /CommentsQuestions /CommentsHollie White

hcwhite1@email.unc.edu

Ryan Scherle

res20@duke.edu

Jane Greenberg

janeg@email.unc.edu