1. Ontology Engineering: Tools and Methodologies
Ian Horrocks
Information Management Group
School of Computer Science
University of Manchester

2. Tutorial Resources

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
“Categories”: Aristotle, 350 BC.
The oldest known tree diagram was drawn in the 3rd century AD by the Greek philosopher Porphyry in his commentary on Aristotle's categories. Figure 2 shows a version of the Tree of Porphyry, as it was drawn by the 13th century logician Peter of Spain. It illustrates the subcategories under Substance, which is called the supreme genus or the most general supertype.

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
In Information Science, an ontology is the product of an attempt to formulate an exhaustive and rigorous conceptual schema about a domain. This domain does not have to be the complete knowledge of that topic, but purely a domain of interest decided upon by the creator of the ontology.
An ontology is typically a hierarchical data structure containing all the relevant entities and their relationships and rules within that domain (e.g., a domain ontology). However, computational ontology does not have to be hierarchical at all. The computer science usage of the term ontology is derived from the much older usage of the term ontology in philosophy.
An ontology which is not tied to a particular problem domain but attempts to describe general entities is known as a foundation ontology or upper ontology. Typically, more specialized domain specific schemata must be created to make the data useful for real world decisions.

6. Example Ontology (Protégé)

7. The Web Ontology Language OWL
Development and standardisation of (Semantic) Web ontology languages has been responsible for huge increase in the development and application of ontologies.

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 DAML+OIL
OWL now a W3C recommendation (i.e., a standard)
OWL is a family of 3 languages: OWL Lite, OWL DL and OWL Full
OIL, DAML+OIL 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.:
Object oriented style of modelling
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)
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
Cat
Animal
IS-A
has-color
Black
Felix
Mat
sits-on
[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 can’t 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)
Pellet
KAON2
CEL

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., >2hasChild, 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 […]”
Heath-Robinson approach to language design; big tree is undecidability.

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 >1hasChild,
HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)}
An ABox is a set of “data” axioms (ground facts), e.g.:
{John:HappyParent,
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):
<owl:Class>
<owl:intersectionOf rdf:parseType=" collection">
<owl:Class rdf:about="#Parent"/>
<owl:Restriction>
<owl:onProperty rdf:resource="#hasChild"/>
<owl:allValuesFrom>
<owl:unionOf rdf:parseType=" collection">
<owl:Class rdf:about="#Intelligent"/>
<owl:Class rdf:about="#Athletic"/>
</owl:unionOf>
</owl:allValuesFrom>
</owl:Restriction>
</owl:intersectionOf>
</owl:Class>

20. Ontology Reasoning
Development and standardisation of (Semantic) Web ontology languages has been responsible for huge increase in the development and application of ontologies.

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
Banana split is a kind of fruit sundae?

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
Graphic showing protein functional domains (sequences of amino acids?); the identifying characteristics of different proteins.

27. Medicine Central Sulcus Parietal Lobe Frontal Lobe Occipital Lobe
Building/maintaining terminologies such as Snomed, NCI, Galen and FMA
Used, e.g., for semi-automated annotation of MRI images
- Example from project to (semi-) automate the annotation of MRI images of the brain
- FMA derived ontology used to capture knowledge of brain anatomy
Frontal Lobe
Temporal Lobe
Parietal Lobe
Occipital
Lobe
Central Sulcus
Lateral Sulcus

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 ESWC’07 (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
Development and standardisation of (Semantic) Web ontology languages has been responsible for huge increase in the development and application of ontologies.

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
CEL

35. Tools and Infrastructure
Editors/environments
Oiled, Protégé, Swoop, Construct, Ontotrack, …
Reasoning systems
Cerebra, FaCT++, Kaon2, Pellet, Racer, …
Design methodologies
Modularity, foundational ontologies, etc.
Entity
Substantial
Quality
Event
Achievement
Stative
Accomplishment
Perdurant
Endurant

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
Clear that OWL isn’t expressive enough for all applications; users always want/need more

38. Demo Ontologies GALEN NCI http://www.mindswap.org/2003/CancerOntology
NCI
Tambis

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

47. Inferred Knowledge A trivial subsumption: Another example:
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 PathologicalBodyProcesses?

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:
~ 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:
Maximum depth of the tree: 19
Classes with multiple inheritance: 4636
Browse through it!
The property hierarchy:
No properties with multiple inheritance

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 >
Person u >2.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. 1 CysticFibrosis v Fibrosis u 9locatedIn.Pancreas u Q
1 CysticFibrosis v Fibrosis u 9locatedIn.Pancreas u
9hasOrigin.GeneticOrigin
2 GeneticFibrosis v Fibrosis u 9hasOrigin.GeneticOrigin
3 Fibrosis u 9 locatedIn. Pancreas v GeneticFibrosis
4 GeneticFibrosis v GeneticDisorder
P [ Q ² > v Project
Q ² CysticFibrosis v Genetic Disorder
P [ Q ² CysticFibProject v GenDisorderProject
P [ Q ² > v 9 hasFocus.>
P [ Q ² GeneticFibrosis t GeneticDisorder v ? P
1 GenDisorderProject = Project u 9hasFocus.GeneticDisorder
2 CysticFibProject = Project u 9hasFocus.CysticFibrosis
3 9hasFocus.> 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 and Research 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 > expenditure)
OWL 1.1 (see
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
Clear that OWL isn’t expressive enough for all applications; users always want/need more

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 > expenditure)
OWL 1.1 (see
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
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]
Clear that OWL isn’t expressive enough for all applications; users always want/need more

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 R&D 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. Thank you for listening
Any questions?
Resources:
FaCT++ system (open source)
OWL
OWL Experiences and Directions Workshop
Protégé
OWL 1.1 Proposal