1. Controlled Vocabularies: What, Why, How?
Vocabulary Workshop, RAL, February 25, 2009
Controlled Vocabularies: What, Why, How?

2. Metadata
Love it or hate it without metadata automated data handling isn’t possible
For automated data handling to be possible across distributed data sources metadata standards are required
Standardised metadata comprises fields that represent real world entities such as location, time, phenomena, etc.

3. Metadata These fields need to be populated
Plaintext may be used. Makes population easy, but it’s next to useless.
Some real examples:
A wide variety of chemical and biological parameters
Amplitude de l'echo retrodiffuse
Cu, Zn, Fe, Pb, Cd, Cr, Ni in biota
MACR0-MEIOFAUNA,SED BIOCHEMISTRY,ZOOPLANKTON, CILIATES,BACT CELLS,BACT BIOMASS,LEUCINE UPT,PRIM. PROD,METABOL, COCCOLITH
Plaintext should be confined to abstracts

4. Controlled Vocabularies
Much better to use concepts labelled using universally agreed terms that have universally agreed meanings
A collection of concepts designed to populate a given metadata field may be called a controlled vocabulary
Controlled vocabularies
Ensure consistent spellings
Ensure consistent syntax
Well-managed controlled vocabularies
Prevent metadata misunderstandings
Maintain a static relationship between metadata fields and the real world

5. Thesuari
Concepts within a controlled vocabulary may be semantically connected using simple relationships:
Blue broader colour
Colour narrower blue
Colour related pigmentation
Concepts from different controlled vocabularies describing the same type of thing may be semantically connected using simple mapping relationships:
Bacillariophycaea exactMatch diatoms
IPTS68 temperature closeMatch ITS90 temperature
Nutrients in rivers relatedMatch nitrate in water bodies
Salinity broadMatch physical oceanography
Physical oceanography narrowMatch salinity
The results may termed thesauri

6. Ontologies
But what if the controlled vocabularies describe different types of thing?
We can relate them by increasing the semantic richness of the relationships
For example:
We could have a controlled vocabulary of instruments
We could also have a controlled vocabulary of parameters

7. Ontologies We can link these up using relationships such as:
Themosalinograph measures salinity
Fluorometer measures chlorophyll
Air temperature measuredBy psychrometer
The result may be termed an ontology

8. Ontologies Ontology relationships are:
Semantically rich
Potentially abundant
Software agents need to have some relationship understanding to exploit the knowledge encoded in the ontology
This is achieved through relationships describing relationships called rules

9. Knowledge Representation
Relationships between concepts may be expressed using Resource Description Framework (RDF)
W3C standard XML encoding having ‘triples’ as its basic building block
Each triple has a subject, a predicate and an object. For example:
Colour related pigmentation
Thermosalinograph measures salinity
Familiar?

10. Knowledge Representation
Controlled vocabularies (concept collections) and thesauri may be represented using the Simple Knowledge Organization System (SKOS)
W3C standard XML schema based on RDF
Jointly developed by STFC and Manchester University Computer Science
2008 version is the one to use

11. Knowledge Representation
<?xml version="1.0" ?>
- <rdf:RDF xmlns:rdf=" xmlns:skos=" xmlns:dc="
- <skos:Concept rdf:about="
  <skos:externalID>SDN:P011:116:TEMPS901</skos:externalID>
  <skos:prefLabel>Temperature (ITS-90) of the water column by CTD or STD</skos:prefLabel>
  <skos:altLabel>CTDTmp90</skos:altLabel>
  <skos:definition>Unavailable</skos:definition>
  <dc:date> T10:45: </dc:date>
  <skos:broadMatch rdf:resource=" />
  </skos:Concept>
  </rdf:RDF>

12. Knowledge Representation
Ontologies may be represented using Web Ontology Language (OWL)
W3C standard XML schema based on RDF
Example OWL document
Alternative simple text encodings are available such as Open Biomedical Ontologies (OBO)
OBO used for NERC-related EnvO ontology

13. Knowledge Management Tools
RDF
Tools abound – see for example
Jena is one of the better known
SKOS
See the SKOS Tool Shed
Note this includes a Protégé plugin

14. Knowledge Management Tools
Protégé with appropriate plugin is the most widely used
There are commercial alternatives such as TopBraid Composer
MMI ( has developed a vocabulary to OWL converter (voc2OWL)
OBO
Text so text tools work
OWL and SKOS converters available

15. Knowledge Management Tools
Mapping
MMI have developed a mapping tool (VINE) to build maps from two OWL files
Visualisation
Concept maps are useful
Cmap tools is very good
FreeMind (open source)