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Building and using ontologies 
Elena Simperl, University of Southampton, UK 
e.simperl@soton.ac.uk @esimperl 
With contributions from “Linked Data: Survey of Adoption”, Tutorial at the 3rd Asian Semantic Web School ASWS 2011, Incheon, South Korea, July 2011 by Aidan Hogan, DERI, IE 
01.09.2014 
1
FUNDAMENTALS 
01.09.2014 
2
Ontologies in Computer Science 
•An ontology defines a domain of interest 
–… in terms of the things you talk about in the domain, their attributes, as well as relationships between them 
•Ontologies are used to 
–Share a common understanding about a domain among people and machines 
–Enable reuse of domain knowledge 
01.09.2014 
3
Requirements analysis motivating scenarios, use cases, existing solutions, 
effort estimation, competency questions, 
application requirements 
Conceptualization 
conceptualization of the model, integration and 
extension of existing solutions 
Implementation implementation of the formal model in a representation 
language 
Knowledge acquisition 
Test (Evaluation) 
Documentation 
Classical ontology engineering process
Example: Project Halo 
01.09.2014 
Images from http://www.projecthalo.com and http://www.inquireproject.com/ 
•Knowledge acquisition from text (books) 
•Professional and crowdsourced annotation 
•Question analysis and answering through a combination of NLP and reasoning techniques
More examples 
01.09.2014 
6 
Images from http://www.ibm.com/watson, http://www.wolframalpha.com/examples/Music.html, http://www.apple.com
Semantic technologies are not THE solution to creating 
intelligent applications, but only one (essential) component 
The Linked Data movement has promoted one approach to create 
and publish semantic data 
–They created momentum for the Semantic Web, as well as several useful data sets 
Rich knowledge representations can be extremely valuable, but 
are costly to achieve 
01.09.2014 
Table from http://www.inquireproject.com/
Our scenario: the Linked Data management life cycle 
01.09.2014 
Image from http://wiki.lod2.eu/
Example: BBC 
•Various micro-sites built and maintained manually 
•No integration across sites in terms of content and metadata 
•Use cases 
–Find and explore content on specific (and related) topics 
–Maintain and re-organize sites 
–Leverage external resources 
•Ontology: One page per thing, reusing DBpedia and MusicBrainz IDs, different labels 
„Design for a world where Google is your homepage, Wikipedia is your CMS, and humans, software developers and machines are your users“ 
http://www.slideshare.net/reduxd/beyond-the-polar-bear
Core ontology engineering activities in our scenario 
•Find ontologies 
•Select ontologies 
•Adjust/extend ontologies 
•Popular activities we do not consider here 
–Requirements analysis 
–Knowledge representation 
–Ontology learning 
–Ontology alignment 
–... 
•See previous summer schools http://videolectures.net/eswc2012_summer_school/ 
•This is not a tutorial about 
–Ontology engineering tools e.g., Protégé (see http://protege.stanford.edu/) 
–Ontology languages e.g., RDFS, OWL 
01.09.2014 
10
FIND ONTOLOGIES 
01.09.2014 
11
Finding existing ontologies 
•Linked Open Vocabularies: over 400 vocabularies, used in the LOD cloud 
–http://lov.okfn.org 
•Protégé Ontologies: several hundreds of ontologies, cross-domain 
–http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library#OWL_ontologies 
•Open Ontology Repository: life sciences and other domains 
–http://ontolog.cim3.net/cgi-bin/wiki.pl?OpenOntologyRepository 
•Dumontier Lab: life sciences ontologies 
–http://dumontierlab.com/index.php?page=ontologies 
•Tones: ontologies used mainly for testing purposes 
–http://rpc295.cs.man.ac.uk:8080/repository/ 
•OBO Foundation Ontologies: hundreds of life sciences ontologies, including mappings 
–http://www.obofoundry.org/ 
•NCBO Bioportal: hundreds of medical ontologies 
–http://bioportal.bioontology.org/ 
•VoCamps 
–http://vocamp.org/wiki/Main_Page
Linked Open Vocabularies 
01.09.2014 
See http://lov.okfn.org
Linked Open Vocabularies (2) 
01.09.2014 
14
Table from http://dublincore.org/documents/dcmi-terms/ 
Dublin Core
Image (originally found) at http://www.deri.ie/fileadmin/images/blog/: Breslin 
Friend Of A Friend
PROV-O 
02.09.2014 
17 
Image from http://www.w3.org/TR/2012/WD-prov-o-20120724/
See http://wiki.dbpedia.org/ 
Classes and properties for Wikipedia export (infoboxes), regularly updated 
DBpedia
Wikidata 
01.09.2014 
See http://www.wikidata.org/
Freebase 
http://www.freebase.com
Image from http://rdfs.org/sioc/spec/:;Bojārs, Breslin et al. 
Semantically Interlinked Online Communities
Image from http://www.w3.org/TR/swbp-skos-core-guide: Miles, Brickley 
Simple Knowledge Organization System
Image from http://code.google.com/p/baetle/wiki/DoapOntology: Breslin 
Description Of A Project
Image from http://musicontology.com/:Raimond, Giasson 
Music Ontology
WordNet 
http://www.w3.org/TR/wordnet-rdf/
schema.org 
•Collection of schemas to mark-up structured content in HTML pages 
See also http://schema.org/docs/full.html
Image from http://www.heppnetz.de/projects/goodrelations/primer/:; Hepp 
GoodRelations
Life sciences and healthcare 
http://www.obofoundry.org/
Getty vocabularies 
http://www.getty.edu/research/tools/vocabularies/lod/index.html
SELECT ONTOLOGIES 
01.09.2014 
30
Selecting relevant ontologies 
•Key: domain and usage 
–There are many different points of view upon a domain 
–Use popular ontologies 
•You might need to adjust/expand an existing ontology to 
–Lexicalization 
–Implementation language (e.g., RDFS, OWL, frames, SKOS) 
–Level of granularity 
–Level of expressivity 
–Instance data 
•Be aware of/that 
–Imports: transitive dependency between ontologies 
–Changes in imported ontologies can result in inconsistencies and changes of meanings and interpretations, as well as computational aspects
ADJUST/EXPAND 
Brief introduction to ontology conceptualization 
01.09.2014 
32
Basics 
•Ontological primitives in this tutorial 
•Classes 
•Instances 
•Attributes 
•Relationships 
•Literals 
•In real applications 
–Ontology languages with different degrees of formality and support for 
•Different types of nodes 
•Different types of edges 
•Built-in features of nodes and edges 
–Nodes and edges may come from different ontologies 
–(Ideally) provenance metadata attached to nodes and edges 
01.09.2014 
33
Example: OWL 
•Classes 
•Instances 
–Set of classes is not always disjunct from set of instances 
•Datatype properties 
•Object properties 
•Constraints 
–Cardinality 
–Range constraints (all values, some values etc.) 
•Others 
–Imports 
–Annotations 
–… 
02.09.2014 
34
Classes 
•A class represents a set of instances 
•A class should be cohesive, meaninfully named, and relevant 
•Classes represent domain concepts and not the words that denote these concepts 
–Synonyms for the same concept do not represent different classes 
MusicArtist 
MusicalWork 
performs
Classes (2) 
•Typically nouns and nominal phrases, but not restricted to them 
–Verbs can be modeled as classes, if the emphasis is on the process as a whole rather than the actual execution 
–No pronouns 
isIll 
0/1 
Person 
Person 
IllnessEpisode 
isAffectedBy
Cohesiveness 
•A class should represent one thing, all of that thing and nothing but that thing 
–Why: Reusability, maintenance, see also OO design 
•You can prove cohesion by giving the class a representative name, typically nouns 
•No plural form, e.g, Albums 
•No others, utilities etc. 
•On a related note: avoid ambiguous terms 
–Manager, handler, processor, list, information, item, data etc. 
37
Instances 
•Entities of a certain type 
–Abstract entities (e.g., Jazz music) are allowed 
•Issues 
–Distinction between classes and instances 
•Example: Stradivarius 
–Choice of the most appropriate class 
•Example: Violetta Valery 
•Identity vs individuality: entities may change values, but remain members of the same class 
–Example: Age of child vs person 
01.09.2014 
38
Characterizing classes 
•Two types of principal characteristics 
–‚Measurable‘ properties of a class: attributes 
–Inter-entity connections: relationships associations 
Image 
Color 
hasColor 
Image 
hasColor 
{red, blue, green,…}
Attributes 
•An attribute is a measurable property of a class 
–Scalar values: choice from a range of possibilities 
–Attributes do NOT exhibit identity 
40 
Temperature 
Measurement 
hasValue 
Temperature 
hasValue 
SomeValue (e.g., 42C, ‘low’) 
Unit 
SomeValue
Relationships 
41 
MusicArtist 
MusicalWork 
MusicalWork 
similar_to 
* 
1 
Performance 
MusicalWork 
0..1 
0..* 
MusicArtist 
Composition 
* 
* 
composes 
Some instances of a class hold a relationship with some instances of another class
Class hierarchy 
•A subclass of a class represents a concept that is a “kind of” the concept that the superclass represents 
•A subclass has 
–Additional properties 
–Restrictions different from those of the superclass, or 
–Participates in different relationships than the superclasses 
•Multiple inheritance may be possible
Class hierarchy (2) 
•All the siblings in the hierarchy (except for the ones at the root) must be at the same level of generality 
•If a class has only one direct subclass there may be a modeling problem or the ontology is not complete 
•If there are more than a dozen subclasses for a given class then additional intermediate categories might be necessary 
•Roles are not subclasses 
•Application dependent or subjective 
–Example: Artist and person 
–Example: Rectangle and square 
02.09.2014 
43
Formal properties of ontologies 
•Identity 
–Example: triangle as three edges of the same length vs edge length and angle 
–Example: the same clay vs the same statue 
–See also primary keys in ER modeling 
•Types and roles 
–Roles hold because an instance happens to participate in some relationship with another instance (at some point in time), and not because they care essential to identify these instances 
–Example: Person vs student vs employee 
•Dependence 
–Existence depends on other instance 
–Example: Student and university 
•Concreteness 
–Has physical location (not necessarily real) 
–Example: Violetta Valery 
•Unity 
–Is identified by the sum of its parts 
–Example: Piece of stone vs person vs pile of stones 
•These properties are inherited along by subclasses and instances 
•Used to 
–Test ontological consistency 
–Avoid unintended inferences 
–Improve extendibility 
–Improve reusability 
•See also 
–OntoClean (http://en.wikipedia.org/wiki/OntoClean) 
01.09.2014 
44
Ontology design paterns 
Content from http://ontologydesignpatterns.org/
Assignments 
(in your free time) 
01.09.2014 
46
Modeling: Unstructured to structured 
The current configuration of the “Red Hot Chili Peppers” are: Anthony Kiedis (vocals), Flea (bass, trumpet, keyboards, and vocals), John Frusciante (guitar), and Chad Smith (drums). The line-up has changed a few times during they years, Frusciante replaced Hillel Slovak in 1988, and when Jack Irons left the band he was briefly replaced by D.H. Peligo until the band found Chad Smith. In addition to playing guitars for Red hot Chili Peppers Frusciante also contributed to the band “The Mars Volta” as a vocalist for some time. 
From September 2004, the Red Hot Chili Peppers started recording the album “Stadium Arcadium”. The album contains 28 tracks and was released on May 5 2006. It includes a track of the song “Hump de Bump”, which was composed in January 26, 2004. The critic Crian Hiatt defined the album as "the most ambitious work in his twenty- three-year career". On August 11 (2006) the band gave a live performance in Portland, Oregon (US), featuring songs from Stadium Arcadium and other albums.
Modeling: different encodings 
•Encode using the notation introduced in the tutorial 
01.09.2014 
48 
Image from http://www.jfsowa.com/ontology/
EUCLID - Providing Linked Data 
49 
@euclid_project 
euclidproject 
euclidproject 
http://www.euclid-project.eu 
Other channels 
eBook 
Course

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Tutorial: Building and using ontologies - E.Simperl - ESWC SS 2014

  • 1. Building and using ontologies Elena Simperl, University of Southampton, UK e.simperl@soton.ac.uk @esimperl With contributions from “Linked Data: Survey of Adoption”, Tutorial at the 3rd Asian Semantic Web School ASWS 2011, Incheon, South Korea, July 2011 by Aidan Hogan, DERI, IE 01.09.2014 1
  • 3. Ontologies in Computer Science •An ontology defines a domain of interest –… in terms of the things you talk about in the domain, their attributes, as well as relationships between them •Ontologies are used to –Share a common understanding about a domain among people and machines –Enable reuse of domain knowledge 01.09.2014 3
  • 4. Requirements analysis motivating scenarios, use cases, existing solutions, effort estimation, competency questions, application requirements Conceptualization conceptualization of the model, integration and extension of existing solutions Implementation implementation of the formal model in a representation language Knowledge acquisition Test (Evaluation) Documentation Classical ontology engineering process
  • 5. Example: Project Halo 01.09.2014 Images from http://www.projecthalo.com and http://www.inquireproject.com/ •Knowledge acquisition from text (books) •Professional and crowdsourced annotation •Question analysis and answering through a combination of NLP and reasoning techniques
  • 6. More examples 01.09.2014 6 Images from http://www.ibm.com/watson, http://www.wolframalpha.com/examples/Music.html, http://www.apple.com
  • 7. Semantic technologies are not THE solution to creating intelligent applications, but only one (essential) component The Linked Data movement has promoted one approach to create and publish semantic data –They created momentum for the Semantic Web, as well as several useful data sets Rich knowledge representations can be extremely valuable, but are costly to achieve 01.09.2014 Table from http://www.inquireproject.com/
  • 8. Our scenario: the Linked Data management life cycle 01.09.2014 Image from http://wiki.lod2.eu/
  • 9. Example: BBC •Various micro-sites built and maintained manually •No integration across sites in terms of content and metadata •Use cases –Find and explore content on specific (and related) topics –Maintain and re-organize sites –Leverage external resources •Ontology: One page per thing, reusing DBpedia and MusicBrainz IDs, different labels „Design for a world where Google is your homepage, Wikipedia is your CMS, and humans, software developers and machines are your users“ http://www.slideshare.net/reduxd/beyond-the-polar-bear
  • 10. Core ontology engineering activities in our scenario •Find ontologies •Select ontologies •Adjust/extend ontologies •Popular activities we do not consider here –Requirements analysis –Knowledge representation –Ontology learning –Ontology alignment –... •See previous summer schools http://videolectures.net/eswc2012_summer_school/ •This is not a tutorial about –Ontology engineering tools e.g., Protégé (see http://protege.stanford.edu/) –Ontology languages e.g., RDFS, OWL 01.09.2014 10
  • 12. Finding existing ontologies •Linked Open Vocabularies: over 400 vocabularies, used in the LOD cloud –http://lov.okfn.org •Protégé Ontologies: several hundreds of ontologies, cross-domain –http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library#OWL_ontologies •Open Ontology Repository: life sciences and other domains –http://ontolog.cim3.net/cgi-bin/wiki.pl?OpenOntologyRepository •Dumontier Lab: life sciences ontologies –http://dumontierlab.com/index.php?page=ontologies •Tones: ontologies used mainly for testing purposes –http://rpc295.cs.man.ac.uk:8080/repository/ •OBO Foundation Ontologies: hundreds of life sciences ontologies, including mappings –http://www.obofoundry.org/ •NCBO Bioportal: hundreds of medical ontologies –http://bioportal.bioontology.org/ •VoCamps –http://vocamp.org/wiki/Main_Page
  • 13. Linked Open Vocabularies 01.09.2014 See http://lov.okfn.org
  • 14. Linked Open Vocabularies (2) 01.09.2014 14
  • 16. Image (originally found) at http://www.deri.ie/fileadmin/images/blog/: Breslin Friend Of A Friend
  • 17. PROV-O 02.09.2014 17 Image from http://www.w3.org/TR/2012/WD-prov-o-20120724/
  • 18. See http://wiki.dbpedia.org/ Classes and properties for Wikipedia export (infoboxes), regularly updated DBpedia
  • 19. Wikidata 01.09.2014 See http://www.wikidata.org/
  • 21. Image from http://rdfs.org/sioc/spec/:;Bojārs, Breslin et al. Semantically Interlinked Online Communities
  • 22. Image from http://www.w3.org/TR/swbp-skos-core-guide: Miles, Brickley Simple Knowledge Organization System
  • 26. schema.org •Collection of schemas to mark-up structured content in HTML pages See also http://schema.org/docs/full.html
  • 28. Life sciences and healthcare http://www.obofoundry.org/
  • 31. Selecting relevant ontologies •Key: domain and usage –There are many different points of view upon a domain –Use popular ontologies •You might need to adjust/expand an existing ontology to –Lexicalization –Implementation language (e.g., RDFS, OWL, frames, SKOS) –Level of granularity –Level of expressivity –Instance data •Be aware of/that –Imports: transitive dependency between ontologies –Changes in imported ontologies can result in inconsistencies and changes of meanings and interpretations, as well as computational aspects
  • 32. ADJUST/EXPAND Brief introduction to ontology conceptualization 01.09.2014 32
  • 33. Basics •Ontological primitives in this tutorial •Classes •Instances •Attributes •Relationships •Literals •In real applications –Ontology languages with different degrees of formality and support for •Different types of nodes •Different types of edges •Built-in features of nodes and edges –Nodes and edges may come from different ontologies –(Ideally) provenance metadata attached to nodes and edges 01.09.2014 33
  • 34. Example: OWL •Classes •Instances –Set of classes is not always disjunct from set of instances •Datatype properties •Object properties •Constraints –Cardinality –Range constraints (all values, some values etc.) •Others –Imports –Annotations –… 02.09.2014 34
  • 35. Classes •A class represents a set of instances •A class should be cohesive, meaninfully named, and relevant •Classes represent domain concepts and not the words that denote these concepts –Synonyms for the same concept do not represent different classes MusicArtist MusicalWork performs
  • 36. Classes (2) •Typically nouns and nominal phrases, but not restricted to them –Verbs can be modeled as classes, if the emphasis is on the process as a whole rather than the actual execution –No pronouns isIll 0/1 Person Person IllnessEpisode isAffectedBy
  • 37. Cohesiveness •A class should represent one thing, all of that thing and nothing but that thing –Why: Reusability, maintenance, see also OO design •You can prove cohesion by giving the class a representative name, typically nouns •No plural form, e.g, Albums •No others, utilities etc. •On a related note: avoid ambiguous terms –Manager, handler, processor, list, information, item, data etc. 37
  • 38. Instances •Entities of a certain type –Abstract entities (e.g., Jazz music) are allowed •Issues –Distinction between classes and instances •Example: Stradivarius –Choice of the most appropriate class •Example: Violetta Valery •Identity vs individuality: entities may change values, but remain members of the same class –Example: Age of child vs person 01.09.2014 38
  • 39. Characterizing classes •Two types of principal characteristics –‚Measurable‘ properties of a class: attributes –Inter-entity connections: relationships associations Image Color hasColor Image hasColor {red, blue, green,…}
  • 40. Attributes •An attribute is a measurable property of a class –Scalar values: choice from a range of possibilities –Attributes do NOT exhibit identity 40 Temperature Measurement hasValue Temperature hasValue SomeValue (e.g., 42C, ‘low’) Unit SomeValue
  • 41. Relationships 41 MusicArtist MusicalWork MusicalWork similar_to * 1 Performance MusicalWork 0..1 0..* MusicArtist Composition * * composes Some instances of a class hold a relationship with some instances of another class
  • 42. Class hierarchy •A subclass of a class represents a concept that is a “kind of” the concept that the superclass represents •A subclass has –Additional properties –Restrictions different from those of the superclass, or –Participates in different relationships than the superclasses •Multiple inheritance may be possible
  • 43. Class hierarchy (2) •All the siblings in the hierarchy (except for the ones at the root) must be at the same level of generality •If a class has only one direct subclass there may be a modeling problem or the ontology is not complete •If there are more than a dozen subclasses for a given class then additional intermediate categories might be necessary •Roles are not subclasses •Application dependent or subjective –Example: Artist and person –Example: Rectangle and square 02.09.2014 43
  • 44. Formal properties of ontologies •Identity –Example: triangle as three edges of the same length vs edge length and angle –Example: the same clay vs the same statue –See also primary keys in ER modeling •Types and roles –Roles hold because an instance happens to participate in some relationship with another instance (at some point in time), and not because they care essential to identify these instances –Example: Person vs student vs employee •Dependence –Existence depends on other instance –Example: Student and university •Concreteness –Has physical location (not necessarily real) –Example: Violetta Valery •Unity –Is identified by the sum of its parts –Example: Piece of stone vs person vs pile of stones •These properties are inherited along by subclasses and instances •Used to –Test ontological consistency –Avoid unintended inferences –Improve extendibility –Improve reusability •See also –OntoClean (http://en.wikipedia.org/wiki/OntoClean) 01.09.2014 44
  • 45. Ontology design paterns Content from http://ontologydesignpatterns.org/
  • 46. Assignments (in your free time) 01.09.2014 46
  • 47. Modeling: Unstructured to structured The current configuration of the “Red Hot Chili Peppers” are: Anthony Kiedis (vocals), Flea (bass, trumpet, keyboards, and vocals), John Frusciante (guitar), and Chad Smith (drums). The line-up has changed a few times during they years, Frusciante replaced Hillel Slovak in 1988, and when Jack Irons left the band he was briefly replaced by D.H. Peligo until the band found Chad Smith. In addition to playing guitars for Red hot Chili Peppers Frusciante also contributed to the band “The Mars Volta” as a vocalist for some time. From September 2004, the Red Hot Chili Peppers started recording the album “Stadium Arcadium”. The album contains 28 tracks and was released on May 5 2006. It includes a track of the song “Hump de Bump”, which was composed in January 26, 2004. The critic Crian Hiatt defined the album as "the most ambitious work in his twenty- three-year career". On August 11 (2006) the band gave a live performance in Portland, Oregon (US), featuring songs from Stadium Arcadium and other albums.
  • 48. Modeling: different encodings •Encode using the notation introduced in the tutorial 01.09.2014 48 Image from http://www.jfsowa.com/ontology/
  • 49. EUCLID - Providing Linked Data 49 @euclid_project euclidproject euclidproject http://www.euclid-project.eu Other channels eBook Course