Ontology Engineering: Tools and Methodologies

1
Ontology Engineering Tools and Methodologies

• Ian Horrocks
• lthorrocks_at_cs.man.ac.ukgt
• Information Management Group
• School of Computer Science
• University of Manchester

2
Tutorial Resources

• http//www.cs.man.ac.uk/horrocks/nsd07/

3
Ontologies
4
Ontology Origins and History

• In Philosophy, fundamental branch of metaphysics
• Studies being or existence and their basic
  categories
• Aims to find out what entities and types of
  entities exist

5
Ontology in Information Science

• An ontology is an engineering artefact consisting
  of
• A vocabulary used to describe (a particular view
  of) some domain
• An explicit specification of the intended meaning
  of the vocabulary.
• Often includes classification based information
• Constraints capturing background knowledge about
  the domain
• Ideally, an ontology should
• Capture a shared understanding of a domain of
  interest
• Provide a formal and machine manipulable model

6
Example Ontology (Protégé)
7
The Web Ontology Language OWL
8
OWL History

• Semantic Web led to requirement for a web
  ontology language
• set up Web-Ontology (WebOnt) Working
  Group
• WebOnt developed OWL language
• OWL based on earlier languages RDF, OIL and
  DAMLOIL
• OWL now a W3C recommendation (i.e., a standard)
• OWL is a family of 3 languages OWL Lite, OWL DL
  and OWL Full
• OIL, DAMLOIL and OWL (DL Lite) based on
  Description Logics
• Many OWL DL/Lite tools ontologies
• Relatively few OWL Full tools or ontologies

9
What Are Description Logics?

• A family of logic based Knowledge Representation
  formalisms
• Descendants of semantic networks and KL-ONE
• Describe domain in terms of concepts (classes),
  roles (properties, relationships) and individuals
• Operators allow for composition of complex
  concepts
• Names can be given to complex concepts, e.g.

HappyParent Parent u 8hasChild.(Intelligent t
Athletic)
10
Why (Description) Logic?

• OWL exploits results of 15 years of DL research
• Well defined (model theoretic) semantics

Quillian, 1967
11
Why (Description) Logic?

• OWL exploits results of 15 years of DL research
• Well defined (model theoretic) semantics
• Formal properties well understood (complexity,
  decidability)

I cant find an efficient algorithm, but neither
can all these famous people.
Garey Johnson. Computers and Intractability A
Guide to the Theory of NP-Completeness. Freeman,
1979.
12
Why (Description) Logic?

• OWL exploits results of 15 years of DL research
• Well defined (model theoretic) semantics
• Formal properties well understood (complexity,
  decidability)
• Known reasoning algorithms

13
Why (Description) Logic?

• OWL exploits results of 15 years of DL research
• Well defined (model theoretic) semantics
• Formal properties well understood (complexity,
  decidability)
• Known reasoning algorithms
• Implemented systems (highly optimised)

KAON2
14
Why the Strange Names?

• Description Logics are a family of KR formalisms
• Mainly distinguished by available operators
• Available operators indicated by letters in name,
  e.g.,
• S basic DL (ALC) plus transitive roles (e.g.,
  ancestor ? R)
• H role hierarchy (e.g., hasDaughter v hasChild)
• O nominals/singleton classes (e.g., Italy)
• I inverse roles (e.g., isChildOf hasChild)
• N number restrictions (e.g., gt2hasChild,
  63hasChild)
• Basic DL role hierarchy nominals inverse
  NR SHOIN
• The basis for OWL-DL
• SHOIN is very expressive, but still decidable
  (just)
• Decidable ? we can build reliable tools and
  reasoners

15
Why (Description) Logic?

• Foundational research was crucial to design of
  OWL
• Informed Working Group decisions at every stage,
  e.g.
• Why not extend the language with feature x,
  which is clearly harmless?
• Adding x would lead to undecidability - see
  proof in

16
Class/Concept Constructors

• C is a concept (class) P is a role (property) x
  is an individual name
• XMLS datatypes as well as classes in 8P.C and
  9P.C
• Restricted form of DL concrete domains

17
Knowledge Base / Ontology Axioms
18
Knowledge Base / Ontology

• A TBox is a set of schema axioms (sentences),
  e.g.
• Parent v Person u gt1hasChild,
• HappyParent Parent u 8hasChild.(Intelligent t
  Athletic)
• An ABox is a set of data axioms (ground facts),
  e.g.
• JohnHappyParent,
• John hasChild Mary
• An OWL ontology is just a SHOIN KB

19
OWL RDF/XML Exchange Syntax
E.g., Parent u 8hasChild.(Intelligent t Athletic)

• ltowlClassgt
• ltowlintersectionOf rdfparseType"
  collection"gt
• ltowlClass rdfabout"Parent"/gt
• ltowlRestrictiongt
• ltowlonProperty rdfresource"hasChild"/gt
• ltowlallValuesFromgt
• ltowlunionOf rdfparseType" collection"gt
• ltowlClass rdfabout"Intelligent"/gt
• ltowlClass rdfabout"Athletic"/gt
• lt/owlunionOfgt
• lt/owlallValuesFromgt
• lt/owlRestrictiongt
• lt/owlintersectionOfgt
• lt/owlClassgt

20
Ontology Reasoning
21
Why Ontology Reasoning?

• Given key role of ontologies in many
  applications, it is essential to provide tools
  and services to help users
• Design and maintain high quality ontologies,
  e.g.
• Meaningful all named classes can have instances

22
Why Ontology Reasoning?

• Given key role of ontologies in many
  applications, it is essential to provide tools
  and services to help users
• Design and maintain high quality ontologies,
  e.g.
• Meaningful all named classes can have instances
• Correct captures intuitions of domain experts

23
Why Ontology Reasoning?

• Given key role of ontologies in many
  applications, it is essential to provide tools
  and services to help users
• Design and maintain high quality ontologies,
  e.g.
• Meaningful all named classes can have instances
• Correct captures intuitions of domain experts
• Minimally redundant no unintended synonyms

?
Banana split
Banana sundae
24
Why Ontology Reasoning?

• Given key role of ontologies in many
  applications, it is essential to provide tools
  and services to help users
• Design and maintain high quality ontologies,
  e.g.
• Meaningful all named classes can have instances
• Correct captures intuitions of domain experts
• Minimally redundant no unintended synonyms
• Answer queries, e.g.
• Find more general/specific classes
• Retrieve individuals/tuples matching
  a given query

25
Ontology Applications
26
e-Science

• E.g., Open Biomedical Ontologies Consortium (GO,
  MGED)
• Used, e.g., for in silico investigations
  relating theory and data
• E.g., relating data on phosphatases to (model of)
  biological knowledge

27
Medicine

• Building/maintaining terminologies such as
  Snomed, NCI, Galen and FMA
• Used, e.g., for semi-automated annotation of MRI
  images

28
Organising Complex Information

• E.g., UN-FAO, NASA, Ordnance Survey, General
  Motors, Lockheed Martin,

29
Organising Complex Information

• E.g., UN-FAO, NASA, Ordnance Survey, General
  Motors, Lockheed Martin,

30
OWL Experiences and Directions

• Workshop at ESWC07 (Innsbruck, Austria, 6-7
  June)
• Brings together users, implementors and
  researchers
• Submissions include
• Enterprise Integration (Mitre)
• Product development (Lockheed Martin)
• Role based access control (NASA)
• Healthcare (SNOMED)
• Agriculture and fisheries (UN Food Agriculture
  Organization)
• Oral Medicine (Chalmers)

31
Ontology Engineering
32
Ontology Engineering Tasks

• Typical tasks in Ontology Engineering
• author concept descriptions
• refine the ontology
• manage errors
• integrate different ontologies
• (partially) reuse ontologies
• These tasks are highly challenging need for
• tool infrastructure support
• design methodologies

33
Tools and Infrastructure

• Editors/environments
• Protégé, Swoop, TopBraid Composer, Construct,
  Ontotrack,

34
Tools and Infrastructure

• Editors/environments
• Oiled, Protégé, Swoop, Construct, Ontotrack,
• Reasoning systems
• Cerebra, FaCT, Kaon2, Pellet, Racer,

Pellet
KAON2
35
Tools and Infrastructure

• Editors/environments
• Oiled, Protégé, Swoop, Construct, Ontotrack,
• Reasoning systems
• Cerebra, FaCT, Kaon2, Pellet, Racer,
• Design methodologies
• Modularity, foundational ontologies, etc.

36
Development Maintenance
37
Development Environments

• Most widely used free to download tools are
• Protégé (Stanford / Manchester) -- be sure to get
  v4.x
• Swoop (UMD / Clark Parsia)
• Commercial tools include
• TopBraid, RacerPro,
• Facilities typically include
• Range of display modes and editing features
• Visualisation
• Consistency and subsumption checking
• Useful extras may include
• Debugging and explanation
• Repair
• Integration and/or partitioning

http//code.google.com/p/swoop/
http//protege.stanford.edu/
38
Demo Ontologies

• GALEN
• http//www.cs.man.ac.uk/horrocks/OWL/Ontologies/g
  alen.owl
• NCI
• http//www.mindswap.org/2003/CancerOntology
• Tambis
• http//www.cs.man.ac.uk/horrocks/OWL/Ontologies/t
  ambis.owl

39
GALEN

• Ontology about medical terms and surgical
  procedures.
• Work started in the 90s within the OpenGALEN
  project.
• Main applications
• Integration of clinical records, and
• decision support.
• GALEN
• is very large (35,000 concepts),
• is fairly expressive (SHIF description logic),
• has not been classified yet by any DL reasoner
• We will look at a smaller version, which
• is still large (3,000 concepts),
• is similarly expressive as full GALEN,
• was first classified by the FaCT system.

40
GALEN The Ontology at a Glance

• Size
• 3,000 classes
• 500 object properties
• no individuals or datatypes
• Expressivity
• 350 General Concept Inclusion Axioms (GCIs).
• Concept constructors
• Conjunction (intersectionOf)
• Existential restrictions (someValuesFrom)
• 150 functional properties
• 26 transitive properties

41
GALEN The (Unclassified) Hierarchies

• The class hierarchy
• Number of subsumption relations 1,978
• Maximum depth of the tree 13
• No multiple inheritance
• The property hierarchy
• 4 properties with multiple inheritance

42
GALEN Concept definitions and GCIs

• Concept definition
• Axiom of the form A C with
• A a concept name
• C a (possibly complex) concept
• A definition assigns a name A to a complex
  concept C
• Some examples
• LungPathology PathologicalCondition u 9
  locativeAttribute.Lung
• RenalTransplant Transplanting u 9
  actsOn.Kindney

43
GALEN Concept definitions and GCIs

• Inclusion axioms
• Axioms of the form A v C
• A is a concept name
• C is a possibly complex concept
• Represent an incomplete (partial) definition
• Examples
• XRayMachine v ImagingDevice
• Candida v Fungus u 9 hasFunction.AerobicMetabolicP
  rocess
• In GALEN, some of these can be very complex
• check out the definitions of Knee Joint and
  Kidney!

44
GALEN Concept definitions and GCIs

• General Concept Inclusion Axioms (GCIs)
• Axioms of the form C D
• C,D can be complex
• May describe general (background) knowledge about
  the ontology
• Examples
• Secretion u 9 actsSpecificallyOn.Leucocidin v
• 9 isFunctionOf.StraphilococcusAureus
• Transport u 9 actsOn.Glucose u 9
  carriesFrom.Blood v
• 9 carriesTo.Cell

45
Classifying GALEN

• Ontology statistics (revisited)
• Number of class subsumption relations 6729
• 1978 of which are told and the rest inferred
• Maximum depth of the class tree 15
• As opposed to 13 in the case of the unclassified
  tree
• Classes with multiple inheritance 408
• All multiple inheritance relations have been
  inferred!
• This was intended in the design of GALEN
• Maximum depth of the property tree 9
• No change with respect to the told tree
• Properties with multiple inheritance 4
• Again, no change with respect to the told tree
• Reasoning is mostly performed on classes and not
  on properties

46
Modeling Choices

• The upper part
• Composed of the domain-independent concepts and
  roles.
• Examples
• TopCategory, DomainCategory, GeneralisedStructure
  
• Shallowly defined (mostly a taxonomy)
• The domain specific part
• Examples
• Plant, LungPathology,
• Richly defined
• Much more than just a taxonomy!

47
Inferred Knowledge

• A trivial subsumption
• Why is PathologicalCondition a subclass of
  DomainCategory?
• Simply look at the definition of Pathological
  Condition!
• Another example
• Why is PathologicalBehavior a subclass of
  PathologicalCondition?
• Look at the definition of both classes
• Notice that Behavior is a subclass of
  DomainCategory
• A non-trivial subsumption
• Why is AchalasiaProcesses a PathologicalBodyProces
  ses?

48
Classifying GALEN

• Simple and multiple inheritance
• Focus, for example, on PathologicalBodyProcess
• Navigate to its super-classes
• Visualisation can be useful
• In Swoop we can Fly the mother ship!

49
The NCI Ontology

• Huge bio-medical ontology describing the Cancer
  domain
• Maintained by dozens of domain experts
• Contains information about
• genes,
• diseases,
• drugs,
• research institutions,
• All with a cancer-centric focus

50
NCI The Ontology at a Glance

• Size
• 30.000 classes
• 70 object properties
• no individuals or datatypes
• Expressivity
• Concept constructors
• Conjunction (intersectionOf)
• Existential restrictions (someValuesFrom)
• Axioms
• Definitions (no GCIs)
• Domain and range of properties

51
NCI The (Unclassified) Hierarchies

• The class hierarchy
• Number of subsumption relations 103.232
• Maximum depth of the tree 19
• Classes with multiple inheritance 4636
• Browse through it!
• The property hierarchy
• No properties with multiple inheritance
• Browse through it!

52
Axioms in NCI

• Examples
• Cancer_Gene v Gene u 9 hasFunction.Tumoregenesis
• Alzheimer_Disease v Dementia
• Domain(rAnatomic_Structure_Has_Location)
  Anatomy_Kind
• Range(rTechnique_Has_Purpose)
  Clinical_Or_Research_Activity_Kind

53
The NCI Kinds

• Upper concepts representing the sub-domains of
  NCI
• Examples
• Anatomy.
• Biological processes.
• Chemicals and drugs.
• Organisms
• Properties relating the Kinds

54
NCI

• Partitioning and crop-circles view of the
  partitioning
• Gives an intuition about the different
  sub-domains in NCI, which ones are central, and
  which ones are side domains

55
NCI and GALEN

• The domains of NCI and GALEN overlap. Both
  ontologies define concepts such as
• Anatomical parts bone, tissue, etc.
• Diseases
• Organisms,
• Example
• Check out how Femur is defined in NCI and GALEN
• Different modeling decisions and focus of
  interest

56
Tambis

• TAMBIS is a medical ontology constructed during
  the early days of the Web.
• The intended application was the integrated
  access to information in a set of databases.
• The OWL version was generated from the old format
  using a (buggy) script.

57
Tambis The Ontology at a Glance

• Size
• 400 classes
• 100 object properties
• no individuals or datatypes
• Expressivity
• No General Concept Inclusion Axioms.
• Concept constructors
• Conjunction (intersectionOf)
• Disjunction (unionOf)
• Existential restrictions (someValuesFrom)
• Universal restriction (allValuesFrom)
• Cardinality restrictions
• Axioms
• Definitions (complete and partial)
• Transitive, functional, symmetric and inverse
  properties

58
Tambis the (unclassified) hierarchies

• Subclass relationships 226
• No multiple inheritance
• Maximum depth of class tree 6
• Maximum depth of property tree 2

59
Tambis Example Axioms

• Tambis uses cardinality restrictions profusely
• See definition of anion
• Use of disjunction
• See definition of atom
• Use of universal restrictions
• See definition of book-title
• Use of complex nested restrictions
• See definition of complement-dna
• See definition of gene
• Disjointness axioms
• See definitions of metal, non-metal and metalloid

60
Tambis Classification

• Subclass relationships 600
• compared to 226
• Classes with multiple inheritance 19
• compared to none
• Maximum deph of class tree 7
• compared to 6
• Maximum depth of property tree 2
• 144 unsatisfiable concepts!

61
Tambis Unsatisfiable concepts

• Almost half of the concepts in Tambis are
  unsatisfiable
• The explanations are non-trivial
• E.g., protein-structure and macromolecular-part
• Distinguishing root and derived unsatisfiable
  classes
• derived unsatisfiable classes are unsatisfiable
  because they depend on another unsatisfiable
  concept.
• definition of Enzyme,
• definition of Binding-site
• root unsatisfiable classes contain an inherent
  contradiction
• definition of Metal,
• definition of Non-metal,
• definition of Metalloid

62
Advanced Issues and Design Patterns
63
Qualified Number Restrictions (QCRs)

• Existential restrictions in OWL DL are qualified
• Person u 9hasChild.Male
• Cardinality restrictions can only be qualified
  with gt
• Person u gt2.hasChild
• The lack of QCRs has been identified as a major
  limitation of OWL, especially in biomedical
  applications
• A quadruped is an animal with exactly four parts
  that are legs
• A medical oversight committee is a committee
  which consists of at least five members of which
  two are medical doctors, one is a manager and two
  are members of the public.

64
Qualified Cardinality Restrictions

• Can be approximated using property inclusion
  and property range.
• Quadruped Animal u ( 4 hasLeg)
• hasLeg v hasPart
• Range(hasLeg) Leg

65
Qualified Cardinality Restrictions

• This approximation is unsound in general
• MedicalCommittee Committee u (3 hasMember)
  u 1hasMember.MD u 1 hasMember. MD
• Approximated by
• MedicalCommittee (3 hasMember) u
  1hasMDMember u
• 1hasNotMDMember
• hasMDMember v hasMember
• hasNotMDMember v hasMember
• Range(hasMDMember) MD
• Range(hasNotMDMember) MD

66
Transitive Propagation of Properties

• In OWL, we can express transitive propagation of
  a property
• If Paris is located in France and France is
  located in Europe, then France is located in
  Europe.
• If the hand is a part of the arm and the arm is
  part of the human body, then the hand is a part
  of the human body.
• In OWL, however, we cannot express transitive
  propagation of a property along a different
  property
• If an ulcer is located in the gastric mucosa and
  the gastric mucosa is a part of the stomach, then
  the ulcer is located in the stomach
• If a burn is located in the foot and the foot is
  part of the leg, then the burn is located in the
  leg.

67
Transitive Propagation of Properties

• Various patterns that approximate transitive
  propagation have been proposed and used in
  ontologies.
• Use of the property hierarchy and transitivity
• Part_Of v Located_In
• Transitive(Part_Of)
• This pattern may yield undesired results, since
  part-whole relations may not always imply
  location
• The orange peal is part of the orange, but is it
  located in the orange?

68
Design Methodologies
69
Modularity in Software Engineering

• Typically referred to as the extent to which
  software is divided into components with
• high internal cohesion
• controlled coupling between each other through
  simple interfaces (encapsulation)
• Benefits of modular software design
• software maintainability
• software understandability

70
Modularity in Ontology Engineering

• Benefits of a modular ontology design to
  simplify
• ontology refinement/update
• modifying a module should not lead to
  modifications in parts of the ontology that are
  not conceptually related
• understanding
• relationships between different modules in an
  ontology controlled and well-understood
• integration with other ontologies
• no unexpected consequences
• partial reuse
• reuse only the relevant part/module of an
  ontology

71

Q 1 CysticFibrosis v Fibrosis u
9locatedIn.Pancreas u 9hasOrigin.GeneticOr
igin 2 GeneticFibrosis v Fibrosis u
9hasOrigin.GeneticOrigin 3 Fibrosis u 9
locatedIn. Pancreas v GeneticFibrosis 4
GeneticFibrosis v GeneticDisorder
Q ² CysticFibrosis v Genetic Disorder
P Q ² gt v Project
P Q ² gt v 9 hasFocus.gt
P Q ² GeneticFibrosis t GeneticDisorder v ?
P Q ² CysticFibProject v GenDisorderProject
P 1 GenDisorderProject Project u
9hasFocus.GeneticDisorder 2 CysticFibProject
Project u 9hasFocus.CysticFibrosis 3 9hasFocus.gt
v Project 4 Project u (GeneticFibrosis u
GeneticDisorder) v ? 5 8 hasFocus.CysticFibrosis
v 9hasFocus.GeneticDisorder
72
Foundational Ontologies

• E.g., DOLCE

73
Recent Work andResearch Challenges
74
Increasing Expressive Power

• Complex role inclusion axioms Horrocks, Kutz
  Sattler, KR-06
• E.g., hasLocation partOf v hasLocation
• Concrete domains/datatypes, e.g., Lutz,
  IJCAI-99 Pan et al, ISWC-03
• E.g., value comparison (income gt expenditure)
• OWL 1.1 (see http//webont.org/owl/1.1/)
• Syntactic sugar to make commonly-stated things
  easier to say
• New class property constructors
• Expanded datatype expressiveness
• Meta-modelling constructs
• Semantic-free comments
• Now a W3C Member Submission

75
Increasing Expressive Power

• Complex role inclusion axioms Horrocks, Kutz
  Sattler, KR-06
• E.g., hasLocation partOf v hasLocation
• Concrete domains/datatypes, e.g., Lutz,
  IJCAI-99 Pan et al, ISWC-03
• E.g., value comparison (income gt expenditure)
• OWL 1.1 (see http//webont.org/owl/1.1/)
• Database style keys Lutz et al, JAIR 2004
• E.g., make model chassis-number is a key for
  Vehicles
• Rule language extensions
• W3C RIF WG (see http//www.w3.org/2005/rules/)
• First order extensions (e.g., SWRL) Horrocks et
  al, JWS, 2005
• Hybrid language extensions, e.g., Eiter et al,
  KR-04 Motik et al, ISWC-04 Rosati, JoWS, 2005
• LP/F-Logic/Common Logic Chen et al, JLP, 1993
  de Bruijn et al, WWW-05

76
Improving Scalability

• Optimisation techniques
• Improve performance of DL reasoners, e.g., Sirin
  et al, KR-06
• Reduction to disjunctive Datalog Motik et at,
  KR-04
• Transform SHOIN ontology to DatalogÇ rules
• Use LP techniques to deal with large numbers of
  ground facts
• Hybrid DL-DB systems Horrocks et al, CADE-05
• Use DB to store Abox (individual) axioms
• Cache inferences and use DB queries to
  answer/scope logical queries
• Polynomial time algorithms for sub-ALC logics
• Graph based techniques for EL Baader et al,
  IJCAI-05
• Database techniques for DL-Lite Calvanese et al,
  AAAI-05

77
Summary

• OWL Ontologies provide vocabulary for annotations
• Terms have well defined meaning
• OWL now being used in a wide range of
  applications
• e-Science, medicine, geography, geology,
• Reasoning enabled tools are of crucial importance
  
• For both design and deployment of ontologies
• Large and extremely active RD area
• New and improved tools methodologies constantly
  appearing
• Research challenges remain
• But tools now mature enough for prime time
  applications

78
Acknowledgements

• Thanks to my many friends in the DL and Semantic
  Web communities, in particular
• Alan Rector
• Franz Baader
• Uli Sattler
• The Swoop/Pellet team
• Aditya Kalyanpur
• Evren Sirin
• Bernardo Cuenca Grau
• Bijan Parsia

79
Resources
Thank you for listening
Any questions?

• FaCT system (open source)
• http//owl.man.ac.uk/factplusplus/
• OWL
• http//www.w3.org/TR/owl-features/
• OWL Experiences and Directions Workshop
• http//owled2007.iut-velizy.uvsq.fr/

• Protégé
• http//protege.stanford.edu/plugins/owl/
• OWL 1.1 Proposal
• http//webont.org/owl/1.1/