SlideShare a Scribd company logo
1 of 21
Taxonomy, Ontology,
Folksonomies, and
SKOS
A presentation by Janet Leu for LIS 882



                                          1
Taxonomy
• The word “Taxonomy” is derived from two Greek stems : taxis
  and nomos.

• Taxis – the arrangement or ordering of things
• Nomos – anything assigned, usage or custom, law or
  ordinance.

• Taxonomy is a subject-based classification that arranges the
  terms in a controlled vocabulary , and allows related terms to
  be grouped together and categorized in ways that make it
  easier to find the correct term to use.

• Taxonomy is useful when searching for, or describing, an         2
  object.
A taxonomy is a kind of knowledge map.                      3
                       Chart taken from:
http://www.greenchameleon.com/gc/blog_detail/defining_taxonomy/
Taxonomy = Knowledge Map
• A good taxonomy means the user can immediately understand
  the overall structure or knowledge domain covered by the
  taxonomy.

• A good taxonomy is also comprehensive, predictable, and easy
  to navigate. There is always a hierarchy and controlled
  vocabulary.

• The user will be able to accurately anticipate what types of
  resources they might find where.

• A taxonomy is semantic in the sense that it describes
  relationships between terms in the taxonomy.                   4
Another example of taxonomy
• We start with a
  generalized
  term, and keep
  getting more and
  more specific.
• Almost anything may
  be classified
  according to some
  taxonomic
  scheme, as long as
  there’s a logical           5
  hierarchy.
Ontology
• Ontology is the study of the categories of things that exist or may
  exist in some domain. It’s the exact description of things and their
  relationships.

• An ontology is a formal specification of a shared conceptualization
  (as defined by Tom Gruber).

• In a philosophical sense, ontology is the study of entology and their
  relations. “What kinds of things can exist or can exist in the
  world, and what matter of relations can those things have to each
  other? Ontology is less concerned about what is than what is
  possible.” (as defined by Clay Shirky from semanticweb.org)

• Ontologies are considered one of the pillars of the Semantic Web.
  After an ontology is developed, it is used, reused, maintained, and
  related to other ontologies. Ontologies should be designed with         6
  these tasks in mind.
Modularization of Ontologies
                • Upper, generic, top-level
                  ontology describes general
                  knowledge, such as what is
                  time and what is space.
                • Domain ontology describes a
                  domain, such as publishing or
                  archives domain.
                • Task ontology is ontology
                  suitable for a particular
                  task, such as creating a DC
                  record in XML.
                • Application ontology is
                  developed for a specific
                  application, such as assembling
                  personal computers.
                                                    7
OWL – Web Ontology Language
               • OWL is a Semantic Web
                 Language (or, a Semantic Web
                 Ontology) designed to
                 represent rich, complex
                 things, groups of things, and
                 relationships between things.
               • OWL is built on top of RDF
               • OWL is for processing
                 information on the web
               • OWL is written in XML
               • OWL is a WC3 standard
                 designed to be interpreted by
                 computers, and not to be
                                                 8
                 read by people.
An example of OWL with an RDF graph
 from (http://www.obitko.com/tutorials/ontologies-semantic-web/owl-example-with-rdf-graph.html

For example, Pizza OWL ontology expressed in RDF
triples(subject, predicate, object):

@prefix :
<http://example.com/pizzas.owl#> . @prefix rdf:
<http://www.w3.org/1999/02/22-rdf-syntax-ns#>.

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>
.

@prefix owl: <http://www.w3.org/2002/07/owl#> .

:Pizza rdfs:subClassOf [ a owl:Restriction ; owl:onProperty
:hasBase ; owl:someValuesFrom :PizzaBase ] ;
owl:disjointWith :PizzaBase .

:NonVegetarianPizza owl:equivalentClass [
owl:intersectionOf ( [owl:complementOf

:VegetarianPizza] :Pizza ) ] . :isIngredientOf a
owl:TransitiveProperty , owl:ObjectProperty ;
owl:inverseOf :hasIngredient .




                                                                                                 9
Folksonomies
• Folksonomies is a user-driven approach to organizing information.
• Websites with folksonomies include two basic functions: users can
  add “tags” to information and create navigational links out of those
  tags to help users find and organize that information later.
• Folksonomies address two disadvantages with taxonomies, in that
  the information within folksonomies is organized and maintained by
  users, so very little work has to be done by the designers after
  initially setting up the tagging system.
• Taxonomies can be time-consuming and expensive for design teams
  to implement. As a result, there may be broken taxonomies until
  the there is a complete redesign, and taxonomies may fail to reflect
  the language of users if they are not fully tested with the target
  population.
                                                                         10
• Folksonomies improve usability and decrease support costs.
Websites that use Folksonomies.
• Flickr
• Del.icio.us
• Wordpress
• Tumblr
• Blogspot
• Blogger
                                  11
Folksonomies vs traditional classification

       Folksonomies              Traditional classification
• Doesn’t have structured      • Has structured
  hierarchical organization      hierarchical organization
• Created by users             • Created by
• Utilizes a                     organizational staff
  decentralized, collaborati
  ve view                      • Proposes an
                                 authoritative centralized
• By definition, tagging
  systems lack precision         view
  and currently do not         • Has a high precision and
  provide synonym                aims to avoid ambiguity
  control.                                                    12
Pros and cons of Folksonomies
           PROS                            CONS
• Great for serendipity and    • Not aimed at a target
  browsing                       approach or search
• Relational                   • Not hierarchical
• Matches users’ real needs    • Sometimes the language
                                 isn’t precise enough
  and language
                               • Doesn’t stress the location
• Stresses the learning          aspect as much
  aspect                       • Tagging is not as reliable as
• Tagging is cheaper than a      a controlled vocabulary, or
  controlled vocabulary, and     traditional schemes of
  is better than nothing.        classification.
                                                                 13
SKOS (Simple Knowledge Organization Systems)

• A common data model for sharing and linking knowledge
  organization systems over the Web.
• Many knowledge organization systems, such as taxonomies
  and subject heading systems, share a similar structure and are
  used in similar applications.
• SKOS captures this similarity and makes it explicit, to enable
  data and technology sharing across diverse applications.
• SKOS also provides a standard, low-cost migration path for
  porting existing knowledge organization systems to the
  Semantic Web.
• May be used on its own, or in combination with formal
  knowledge representation languages, like OWL.                    14
SKOS can be used to improve taxonomy.




                                                                                                                          15


                                                                                                                      /
   Sample label relationships in a pre-SKOS taxonomy, from http://www.ibm.com/developerworks/xml/library/x-skostaxonomy
How SKOS can be used to improve taxonomy, part 2




                                                                   16
   http://www.ibm.com/developerworks/xml/library/x-skostaxonomy/
SKOS and LCSH
• The MARC21 Authority format distinguishes between authorized
  (1XX) and non-authorized (4XX) headings.
• SKOS vocabulary provides two properties: skos:prefLabel and
  skos:altLabel.
• These two labels allow a concept to be associated with both
  preferred and alternate natural language labels.
• The SKOS vocabulary allows both authorized and non-authorized
  LCSH headings to be mapped directly to skos:prefLabel and
  skos:altLabel properties in a straightforward manner.
• Semantic relationships in LCSH/MARC easily translated into
  LCSH/SKOS.
• Links in LCSH/MARC use the established heading as references.
• In LCSH/SKOS, conceptual resources are linked together by their   17
  URIs.
Taxonomy Bibliography
Garshol, Lars Marius. (October 26, 2004) Metadata? Thesauri?
Taxonomies? Topic Maps! Retrieved from
http://www.ontopia.net/topicmaps/materials/tm-vs-
thesauri.html

Gasser, Michael. (September 10, 2006). Word Senses and
Taxonomies. Retrieved from
http://www.indiana.edu/~hlw/Meaning/senses.html

Lambe, Patrick. (April 18, 2006). Defining Taxonomy.
Retrieved from
http://www.greenchameleon.com/gc/blog_detail/defining_taxo
nomy/                                                          18
Ontology Bibliography
• Obitko, Marek. Modularization and Ontoligies. Retrieved
  March 1, 2012, from
  http://www.obitko.com/tutorials/ontologies-semantic-
  web/modularization-of-ontologies.html
• Ontology. (n.d.). In Semantic Web Wiki. Retrieved March
  1, 2012, from http://semanticweb.org/wiki/Ontology
• Smith, Michael K., Chris Welty and Deborah L. McGuiness.
  (February 10, 2004). OWL Web Ontology Language Guide.
  Retrieved from http://www.w3.org/TR/owl-guide/
• Sowa, John F. (November 29, 2010. Ontology. Retrieved from
  http://www.jfsowa.com/ontology/
• Welty, Chris. (April 2005). Semantic Web Ontologies.
  Retrieved from
                                                               19
  http://www.daml.org/meetings/2005/04/pi/Ontologies.pdf
Folksonomies Bibliography
• Mathes, Adam. (December 2004). Folksonomies – Cooperative
  Classification and Communication Through Shared Metadata.
  Retrieved from http://www.adammathes.com/academic/computer-
  mediated-communication/folksonomies.html
• Porter, Joshua. (April 26, 2005). Folksonomies: A User-Driven
  Approach to Organizing Content. Retrieved from
  http://www.uie.com/articles/folksonomies/
• Quintarelli, Emanuele. (June 24, 2005). Folksonomies: Power to the
  People. Retrieved from http://www.iskoi.org/doc/folksonomies.htm
• Terdiman, Daniel. (February 1, 2005). Folksonomies Tap People
  Power. Retrieved from
  http://www.wired.com/science/discoveries/news/2005/02/66456?c
  urrentPage=all
                                                                       20
SKOS Bibliography
• DuCharme, Bob. (May 10, 2011). Improve Your Taxonomy
  Management Using the W3C SKOS Standard. Retrieved from
  http://www.ibm.com/developerworks/xml/library/x-
  skostaxonomy/
• Mikhalenko, Peter. (June 22, 2005). Introducing SKOS.
  Retrieved from
  http://www.xml.com/pub/a/2005/06/22/skos.html
• Miles, Alison, and Sean Bechhofer. (August 18. 2009). SKOS
  Simple Knowledge Organization System Reference. Retrieved
  from http://www.w3.org/TR/skos-reference/
• Summers, Ed., Antoine Isaac, Clay Redding, and Dan Krech.
  (2008). LCSH, SKOS and Linked Data. Retrieved from
  http://dcpapers.dublincore.org/ojs/pubs/article/viewFile/916/
  912                                                             21

More Related Content

What's hot

Relationship of information science with library science
Relationship of information science with library scienceRelationship of information science with library science
Relationship of information science with library science
Sadaf Batool
 
Usage of helpful sequence in cc(colon classification)
Usage of helpful sequence in cc(colon classification) Usage of helpful sequence in cc(colon classification)
Usage of helpful sequence in cc(colon classification)
Prakash Das
 

What's hot (20)

Modes of formation of subjects by Gordon Amidu
Modes of formation of subjects by Gordon AmiduModes of formation of subjects by Gordon Amidu
Modes of formation of subjects by Gordon Amidu
 
Marc 21
Marc 21Marc 21
Marc 21
 
Canons for verbal and notational plane
Canons for verbal and notational planeCanons for verbal and notational plane
Canons for verbal and notational plane
 
RDA (Resource Description & Access)
RDA (Resource Description & Access)RDA (Resource Description & Access)
RDA (Resource Description & Access)
 
The Common communication format (CCF)
The Common communication format (CCF)The Common communication format (CCF)
The Common communication format (CCF)
 
Bibliographic description an overview
Bibliographic description an overviewBibliographic description an overview
Bibliographic description an overview
 
Common communication format
Common communication formatCommon communication format
Common communication format
 
Unisist ppt
Unisist pptUnisist ppt
Unisist ppt
 
Dublin core Presentation
Dublin core PresentationDublin core Presentation
Dublin core Presentation
 
Indexing language concept types and characteristics
Indexing language concept types and characteristicsIndexing language concept types and characteristics
Indexing language concept types and characteristics
 
Information storage and retrieval
Information storage and  retrievalInformation storage and  retrieval
Information storage and retrieval
 
IATLIS.pptx
IATLIS.pptxIATLIS.pptx
IATLIS.pptx
 
Library congress subject headings
Library congress subject headings Library congress subject headings
Library congress subject headings
 
BIBLIOMETRICS LAWS
BIBLIOMETRICS LAWSBIBLIOMETRICS LAWS
BIBLIOMETRICS LAWS
 
A brief history of MARC
A brief history of MARCA brief history of MARC
A brief history of MARC
 
Fundamental Category
 Fundamental Category Fundamental Category
Fundamental Category
 
Relationship of information science with library science
Relationship of information science with library scienceRelationship of information science with library science
Relationship of information science with library science
 
Knowledge organization system
Knowledge organization systemKnowledge organization system
Knowledge organization system
 
Usage of helpful sequence in cc(colon classification)
Usage of helpful sequence in cc(colon classification) Usage of helpful sequence in cc(colon classification)
Usage of helpful sequence in cc(colon classification)
 
SLSH ppt
SLSH pptSLSH ppt
SLSH ppt
 

Similar to Taxonomy, ontology, folksonomies & SKOS.

Web 3 final(1)
Web 3 final(1)Web 3 final(1)
Web 3 final(1)
Venky Dood
 
Fri schreiber key_knowledge engineering
Fri schreiber key_knowledge engineeringFri schreiber key_knowledge engineering
Fri schreiber key_knowledge engineering
eswcsummerschool
 
A Comparative Study of Ontology building Tools in Semantic Web Applications
A Comparative Study of Ontology building Tools in Semantic Web Applications A Comparative Study of Ontology building Tools in Semantic Web Applications
A Comparative Study of Ontology building Tools in Semantic Web Applications
dannyijwest
 
A Comparative Study Ontology Building Tools for Semantic Web Applications
A Comparative Study Ontology Building Tools for Semantic Web Applications A Comparative Study Ontology Building Tools for Semantic Web Applications
A Comparative Study Ontology Building Tools for Semantic Web Applications
IJwest
 
A Comparative Study Ontology Building Tools for Semantic Web Applications
A Comparative Study Ontology Building Tools for Semantic Web Applications A Comparative Study Ontology Building Tools for Semantic Web Applications
A Comparative Study Ontology Building Tools for Semantic Web Applications
dannyijwest
 
Hackathon report catalogue-ontology-vocabulary-characteristcs-relevant-to-e...
Hackathon report   catalogue-ontology-vocabulary-characteristcs-relevant-to-e...Hackathon report   catalogue-ontology-vocabulary-characteristcs-relevant-to-e...
Hackathon report catalogue-ontology-vocabulary-characteristcs-relevant-to-e...
Amanda Vizedom
 
Lecture4202011 110420175305-phpapp01
Lecture4202011 110420175305-phpapp01Lecture4202011 110420175305-phpapp01
Lecture4202011 110420175305-phpapp01
Tarek Koudsi
 
Representation of ontology by Classified Interrelated object model
Representation of ontology by Classified Interrelated object modelRepresentation of ontology by Classified Interrelated object model
Representation of ontology by Classified Interrelated object model
Mihika Shah
 

Similar to Taxonomy, ontology, folksonomies & SKOS. (20)

Knowledge engineering and the Web
Knowledge engineering and the WebKnowledge engineering and the Web
Knowledge engineering and the Web
 
Ontologies Fmi 042010
Ontologies Fmi 042010Ontologies Fmi 042010
Ontologies Fmi 042010
 
Web 3 final(1)
Web 3 final(1)Web 3 final(1)
Web 3 final(1)
 
LoCloud Vocabulary Services: Thesaurus management introduction, Walter Koch a...
LoCloud Vocabulary Services: Thesaurus management introduction, Walter Koch a...LoCloud Vocabulary Services: Thesaurus management introduction, Walter Koch a...
LoCloud Vocabulary Services: Thesaurus management introduction, Walter Koch a...
 
Fri schreiber key_knowledge engineering
Fri schreiber key_knowledge engineeringFri schreiber key_knowledge engineering
Fri schreiber key_knowledge engineering
 
The state of KOS in the Linked Data movement
The state of KOS in the Linked Data movementThe state of KOS in the Linked Data movement
The state of KOS in the Linked Data movement
 
Taxonomies and Metadata
Taxonomies and MetadataTaxonomies and Metadata
Taxonomies and Metadata
 
Building OBO Foundry ontology using semantic web tools
Building OBO Foundry ontology using semantic web toolsBuilding OBO Foundry ontology using semantic web tools
Building OBO Foundry ontology using semantic web tools
 
A Comparative Study of Ontology building Tools in Semantic Web Applications
A Comparative Study of Ontology building Tools in Semantic Web Applications A Comparative Study of Ontology building Tools in Semantic Web Applications
A Comparative Study of Ontology building Tools in Semantic Web Applications
 
A Comparative Study Ontology Building Tools for Semantic Web Applications
A Comparative Study Ontology Building Tools for Semantic Web Applications A Comparative Study Ontology Building Tools for Semantic Web Applications
A Comparative Study Ontology Building Tools for Semantic Web Applications
 
A Comparative Study Ontology Building Tools for Semantic Web Applications
A Comparative Study Ontology Building Tools for Semantic Web Applications A Comparative Study Ontology Building Tools for Semantic Web Applications
A Comparative Study Ontology Building Tools for Semantic Web Applications
 
Hackathon report catalogue-ontology-vocabulary-characteristcs-relevant-to-e...
Hackathon report   catalogue-ontology-vocabulary-characteristcs-relevant-to-e...Hackathon report   catalogue-ontology-vocabulary-characteristcs-relevant-to-e...
Hackathon report catalogue-ontology-vocabulary-characteristcs-relevant-to-e...
 
Lecture4202011 110420175305-phpapp01
Lecture4202011 110420175305-phpapp01Lecture4202011 110420175305-phpapp01
Lecture4202011 110420175305-phpapp01
 
SKOS, RDFa, Microformats, Microdata
SKOS, RDFa, Microformats, MicrodataSKOS, RDFa, Microformats, Microdata
SKOS, RDFa, Microformats, Microdata
 
2012.11 - ISWC 2012 - DC - 1
2012.11 - ISWC 2012 - DC - 12012.11 - ISWC 2012 - DC - 1
2012.11 - ISWC 2012 - DC - 1
 
Representation of ontology by Classified Interrelated object model
Representation of ontology by Classified Interrelated object modelRepresentation of ontology by Classified Interrelated object model
Representation of ontology by Classified Interrelated object model
 
OOR--Open-Ontology-Repository--jun2010
OOR--Open-Ontology-Repository--jun2010OOR--Open-Ontology-Repository--jun2010
OOR--Open-Ontology-Repository--jun2010
 
Ontology
OntologyOntology
Ontology
 
SWSN UNIT-3.pptx we can information about swsn professional
SWSN UNIT-3.pptx we can information about swsn professionalSWSN UNIT-3.pptx we can information about swsn professional
SWSN UNIT-3.pptx we can information about swsn professional
 
Ontology Engineering
Ontology EngineeringOntology Engineering
Ontology Engineering
 

Recently uploaded

BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
SoniaTolstoy
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
QucHHunhnh
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
QucHHunhnh
 

Recently uploaded (20)

Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
 
Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
 
Sanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfSanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdf
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
Student login on Anyboli platform.helpin
Student login on Anyboli platform.helpinStudent login on Anyboli platform.helpin
Student login on Anyboli platform.helpin
 
General AI for Medical Educators April 2024
General AI for Medical Educators April 2024General AI for Medical Educators April 2024
General AI for Medical Educators April 2024
 
9548086042 for call girls in Indira Nagar with room service
9548086042  for call girls in Indira Nagar  with room service9548086042  for call girls in Indira Nagar  with room service
9548086042 for call girls in Indira Nagar with room service
 
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communication
 
BAG TECHNIQUE Bag technique-a tool making use of public health bag through wh...
BAG TECHNIQUE Bag technique-a tool making use of public health bag through wh...BAG TECHNIQUE Bag technique-a tool making use of public health bag through wh...
BAG TECHNIQUE Bag technique-a tool making use of public health bag through wh...
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 

Taxonomy, ontology, folksonomies & SKOS.

  • 1. Taxonomy, Ontology, Folksonomies, and SKOS A presentation by Janet Leu for LIS 882 1
  • 2. Taxonomy • The word “Taxonomy” is derived from two Greek stems : taxis and nomos. • Taxis – the arrangement or ordering of things • Nomos – anything assigned, usage or custom, law or ordinance. • Taxonomy is a subject-based classification that arranges the terms in a controlled vocabulary , and allows related terms to be grouped together and categorized in ways that make it easier to find the correct term to use. • Taxonomy is useful when searching for, or describing, an 2 object.
  • 3. A taxonomy is a kind of knowledge map. 3 Chart taken from: http://www.greenchameleon.com/gc/blog_detail/defining_taxonomy/
  • 4. Taxonomy = Knowledge Map • A good taxonomy means the user can immediately understand the overall structure or knowledge domain covered by the taxonomy. • A good taxonomy is also comprehensive, predictable, and easy to navigate. There is always a hierarchy and controlled vocabulary. • The user will be able to accurately anticipate what types of resources they might find where. • A taxonomy is semantic in the sense that it describes relationships between terms in the taxonomy. 4
  • 5. Another example of taxonomy • We start with a generalized term, and keep getting more and more specific. • Almost anything may be classified according to some taxonomic scheme, as long as there’s a logical 5 hierarchy.
  • 6. Ontology • Ontology is the study of the categories of things that exist or may exist in some domain. It’s the exact description of things and their relationships. • An ontology is a formal specification of a shared conceptualization (as defined by Tom Gruber). • In a philosophical sense, ontology is the study of entology and their relations. “What kinds of things can exist or can exist in the world, and what matter of relations can those things have to each other? Ontology is less concerned about what is than what is possible.” (as defined by Clay Shirky from semanticweb.org) • Ontologies are considered one of the pillars of the Semantic Web. After an ontology is developed, it is used, reused, maintained, and related to other ontologies. Ontologies should be designed with 6 these tasks in mind.
  • 7. Modularization of Ontologies • Upper, generic, top-level ontology describes general knowledge, such as what is time and what is space. • Domain ontology describes a domain, such as publishing or archives domain. • Task ontology is ontology suitable for a particular task, such as creating a DC record in XML. • Application ontology is developed for a specific application, such as assembling personal computers. 7
  • 8. OWL – Web Ontology Language • OWL is a Semantic Web Language (or, a Semantic Web Ontology) designed to represent rich, complex things, groups of things, and relationships between things. • OWL is built on top of RDF • OWL is for processing information on the web • OWL is written in XML • OWL is a WC3 standard designed to be interpreted by computers, and not to be 8 read by people.
  • 9. An example of OWL with an RDF graph from (http://www.obitko.com/tutorials/ontologies-semantic-web/owl-example-with-rdf-graph.html For example, Pizza OWL ontology expressed in RDF triples(subject, predicate, object): @prefix : <http://example.com/pizzas.owl#> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>. @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix owl: <http://www.w3.org/2002/07/owl#> . :Pizza rdfs:subClassOf [ a owl:Restriction ; owl:onProperty :hasBase ; owl:someValuesFrom :PizzaBase ] ; owl:disjointWith :PizzaBase . :NonVegetarianPizza owl:equivalentClass [ owl:intersectionOf ( [owl:complementOf :VegetarianPizza] :Pizza ) ] . :isIngredientOf a owl:TransitiveProperty , owl:ObjectProperty ; owl:inverseOf :hasIngredient . 9
  • 10. Folksonomies • Folksonomies is a user-driven approach to organizing information. • Websites with folksonomies include two basic functions: users can add “tags” to information and create navigational links out of those tags to help users find and organize that information later. • Folksonomies address two disadvantages with taxonomies, in that the information within folksonomies is organized and maintained by users, so very little work has to be done by the designers after initially setting up the tagging system. • Taxonomies can be time-consuming and expensive for design teams to implement. As a result, there may be broken taxonomies until the there is a complete redesign, and taxonomies may fail to reflect the language of users if they are not fully tested with the target population. 10 • Folksonomies improve usability and decrease support costs.
  • 11. Websites that use Folksonomies. • Flickr • Del.icio.us • Wordpress • Tumblr • Blogspot • Blogger 11
  • 12. Folksonomies vs traditional classification Folksonomies Traditional classification • Doesn’t have structured • Has structured hierarchical organization hierarchical organization • Created by users • Created by • Utilizes a organizational staff decentralized, collaborati ve view • Proposes an authoritative centralized • By definition, tagging systems lack precision view and currently do not • Has a high precision and provide synonym aims to avoid ambiguity control. 12
  • 13. Pros and cons of Folksonomies PROS CONS • Great for serendipity and • Not aimed at a target browsing approach or search • Relational • Not hierarchical • Matches users’ real needs • Sometimes the language isn’t precise enough and language • Doesn’t stress the location • Stresses the learning aspect as much aspect • Tagging is not as reliable as • Tagging is cheaper than a a controlled vocabulary, or controlled vocabulary, and traditional schemes of is better than nothing. classification. 13
  • 14. SKOS (Simple Knowledge Organization Systems) • A common data model for sharing and linking knowledge organization systems over the Web. • Many knowledge organization systems, such as taxonomies and subject heading systems, share a similar structure and are used in similar applications. • SKOS captures this similarity and makes it explicit, to enable data and technology sharing across diverse applications. • SKOS also provides a standard, low-cost migration path for porting existing knowledge organization systems to the Semantic Web. • May be used on its own, or in combination with formal knowledge representation languages, like OWL. 14
  • 15. SKOS can be used to improve taxonomy. 15 / Sample label relationships in a pre-SKOS taxonomy, from http://www.ibm.com/developerworks/xml/library/x-skostaxonomy
  • 16. How SKOS can be used to improve taxonomy, part 2 16 http://www.ibm.com/developerworks/xml/library/x-skostaxonomy/
  • 17. SKOS and LCSH • The MARC21 Authority format distinguishes between authorized (1XX) and non-authorized (4XX) headings. • SKOS vocabulary provides two properties: skos:prefLabel and skos:altLabel. • These two labels allow a concept to be associated with both preferred and alternate natural language labels. • The SKOS vocabulary allows both authorized and non-authorized LCSH headings to be mapped directly to skos:prefLabel and skos:altLabel properties in a straightforward manner. • Semantic relationships in LCSH/MARC easily translated into LCSH/SKOS. • Links in LCSH/MARC use the established heading as references. • In LCSH/SKOS, conceptual resources are linked together by their 17 URIs.
  • 18. Taxonomy Bibliography Garshol, Lars Marius. (October 26, 2004) Metadata? Thesauri? Taxonomies? Topic Maps! Retrieved from http://www.ontopia.net/topicmaps/materials/tm-vs- thesauri.html Gasser, Michael. (September 10, 2006). Word Senses and Taxonomies. Retrieved from http://www.indiana.edu/~hlw/Meaning/senses.html Lambe, Patrick. (April 18, 2006). Defining Taxonomy. Retrieved from http://www.greenchameleon.com/gc/blog_detail/defining_taxo nomy/ 18
  • 19. Ontology Bibliography • Obitko, Marek. Modularization and Ontoligies. Retrieved March 1, 2012, from http://www.obitko.com/tutorials/ontologies-semantic- web/modularization-of-ontologies.html • Ontology. (n.d.). In Semantic Web Wiki. Retrieved March 1, 2012, from http://semanticweb.org/wiki/Ontology • Smith, Michael K., Chris Welty and Deborah L. McGuiness. (February 10, 2004). OWL Web Ontology Language Guide. Retrieved from http://www.w3.org/TR/owl-guide/ • Sowa, John F. (November 29, 2010. Ontology. Retrieved from http://www.jfsowa.com/ontology/ • Welty, Chris. (April 2005). Semantic Web Ontologies. Retrieved from 19 http://www.daml.org/meetings/2005/04/pi/Ontologies.pdf
  • 20. Folksonomies Bibliography • Mathes, Adam. (December 2004). Folksonomies – Cooperative Classification and Communication Through Shared Metadata. Retrieved from http://www.adammathes.com/academic/computer- mediated-communication/folksonomies.html • Porter, Joshua. (April 26, 2005). Folksonomies: A User-Driven Approach to Organizing Content. Retrieved from http://www.uie.com/articles/folksonomies/ • Quintarelli, Emanuele. (June 24, 2005). Folksonomies: Power to the People. Retrieved from http://www.iskoi.org/doc/folksonomies.htm • Terdiman, Daniel. (February 1, 2005). Folksonomies Tap People Power. Retrieved from http://www.wired.com/science/discoveries/news/2005/02/66456?c urrentPage=all 20
  • 21. SKOS Bibliography • DuCharme, Bob. (May 10, 2011). Improve Your Taxonomy Management Using the W3C SKOS Standard. Retrieved from http://www.ibm.com/developerworks/xml/library/x- skostaxonomy/ • Mikhalenko, Peter. (June 22, 2005). Introducing SKOS. Retrieved from http://www.xml.com/pub/a/2005/06/22/skos.html • Miles, Alison, and Sean Bechhofer. (August 18. 2009). SKOS Simple Knowledge Organization System Reference. Retrieved from http://www.w3.org/TR/skos-reference/ • Summers, Ed., Antoine Isaac, Clay Redding, and Dan Krech. (2008). LCSH, SKOS and Linked Data. Retrieved from http://dcpapers.dublincore.org/ojs/pubs/article/viewFile/916/ 912 21