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Title: 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 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 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/
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