SKOS-2-HIVE

SKOS-2-HIVE UNT workshop

Morning Session Schedule 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

Introductions Hollie White hcwhite1@email.unc.edu

Exploring HIVE http://hive.nescent.org

Section 1: Knowledge Organization andVocabulary Control

Greenberg’s Ontology Continuum Classical view of ILS languages <___|____|_______|______|_____|______|______|_______|________|_____> Simple thesauri/ deeper taxonomies low level full/intricate Key word CV thesauri ontologies ontologies Lists (WordNet)(OWL)

Types of Vocabulary Control From least to most structure • Term lists Controlled 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 Control 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 Libraries 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: 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 Organization 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 identifiers • 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 Relationships Syndetic relationships are the conceptual connections between terms. Three types of syndetic relationships • Hierarchical • Equivalent • Associative

Hierarchical • Level of generality – both preferred terms • BT (broader term) • Birthday cakes BT Cakes • NT (narrower term) • Cakes NT Birthday cakes …remember inheritance

Equivalent • 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

Equivalent • Non-preferred term USE Preferred term • Biological diversification USE Biodiversity • Preferred term UF (used for) Non-preferred term • Biodiversity UF Biological diversification

Associative • 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

Associative • 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 code • 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?Example: Table • Lexical level : Table • Conceptual level :

What is a SKOS Concept? Zygotes BT Ova NT Oocysts RT Hemizygosity RT Reproduction RT Zygosity UF Ookinetes All these relationships make up a SKOS concept

ConceptualizingSKOS See activity in packet

Example 1:Web view of NBII entry

XML Extensible 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 encoding

NBII 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>

CreatingSKOS/XML See activity online

RDF Resource Description Framework “is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadatadata 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 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 has the color blue RDF triple: a subject denoting "the sky“ a predicate denoting "has the color” an object denoting "blue”

Things to know about 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 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>

<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> NBII in SKOS/RDF

Deconstructing SKOS/RDF

<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>

DeconstructingSKOS/RDF For more examples of deconstruction see packet

Constructing SKOS See activities online

Section 3: From SKOS to HIVE

SKOS-2-HIVE

SKOS-2-HIVE

Presentation Transcript

Hive Wars

HIVE

Hive Index

HIVE

HIVE

SKOS-2-HIVE

SKOS

D4: SKOS and HIVE—Enhancing the Creation, Design and Flow of Information

Hive Inspections

SKOS-2-HIVE

SKOS Tutorial Catch

SKOS-2-HIVE

Hive 101: Hive Query Language

SKOS-2-HIVE

SKOS-2-HIVE