Understanding Realworld Ontologies

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Understanding Real-world Ontologies

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Outline

• Analysis of real-world ontologies
• The (simplified) GALEN ontology.
• The National Cancer Institute (NCI) Thesaurus.
• The TAMBIS ontology.
• Advanced issues and design patterns
• Qualified versus unqualified number restrictions.
• Transitive propagation of properties.
• Nominals and pseudo-nominals.

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Analysis of Real-world Ontologies
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GALEN

• Ontology about medical terms and surgical
  procedures.
• Constructed 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
• In this tutorial we use a smaller version, which
• is still large (3000 concepts),
• is similarly expressive as full GALEN,
• was first classified by the FaCT system.

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GALEN The Ontology at a Glance

• Size
• 3000 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

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GALEN The (Unclassified) Hierarchies

• The class hierarchy
• Number of subsumption relations 1978
• Maximum depth of the tree 13
• No multiple inheritance
• Browse through it!
• The property hierarchy
• 4 properties with multiple inheritance
• Browse through it!

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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 Pathology u 9 locativeAttribute.Lu
  ng
• RenalTransplant Transplanting u 9
  actsOn.Kindney

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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 9 hasFunction.AerobicMetabolicProcess
• In GALEN, some of these can be very complex
• check out the definitions of Knee Joint and
  Kidney!

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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
• 9 actsOn.Glucose u Transport u 9
  carriesFrom.Blood v
• 9 carriesTo.Cell

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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

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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!

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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 are Achalasia Processes Pathological Body
  Processes?
• Try!
• If you dont succeed use the pinpointing
  explanation service

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Classifying GALEN

• Simple and multiple inheritance
• Focus, for example, on PathologicalBodyProcess
• Navigate to its super-classes
• Fly the mother ship and see what is going on!

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The NCI Ontology

• Huge bio-medical ontology describing the Cancer
  domain
• Maintained by a dozen of domain experts
• Contains information about
• genes,
• diseases,
• drugs,
• research institutions,
• All with a cancer-centric focus
• Download it!
• http//www.mindswap.org/2003/CancerOntology

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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

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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!

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Axioms in NCI

• Examples
• Cancer_Gene v Gene u 9 hasFunction.Tumoregenesis
• Alzheimer_Disease v Dementia
• Domain(anatomic_Structure_has_Location)
  Anatomy_Kind
• Range(technique_hasPurpose) Clinical_Or_Research
  _Activity_Kind

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The NCI Kinds

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

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NCI

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

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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
• Discuss the different modeling decisions and
  focus of interest

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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 script.

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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

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Tambis the (unclassified) hierarchies

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

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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

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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!

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Tambis Unsatisfiable concepts

• Almost half of the concepts in Tambis are
  unsatisfiable
• The explanations are non-trivial
• Check out 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

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Tambis Repair
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Advanced Issues and Design Patterns
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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 9hasChild.Male
• 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.

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Qualified Cardinality Restrictions

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

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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

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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.

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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 to 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?

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Nominals in OWL-DL

• Define concepts in terms of individuals.
• Two constructs in OWL
• owloneOf, owlhasValue
• owloneOf - Enumeration of individuals.
• WineColor ? red, white, rose
• red, white, rose red t white t rose
• owlhasValue - Value restrictions.
• RedWine ? 9hasColor.red
• RockFan v 9hasIdol.elvis

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Nominals and Pseudo-nominals

• Reasoners traditionally do not support nominals
  (only Aboxes)
• Not enough implementation experience.
• Believed to be hard.
• Decision procedure for SHON in 2001!
• Example Wine ontology
• Used in OWL guide to demonstrate OWL.
• Large number of nominals used.
• No reasoner (even incomplete) could reason with
  it! Only Pellet (very recently)

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Faking Nominals

• Pseudonominals Approximation to nominals

SpanishWine Wine u 9producedIn.spain FrenchWi
ne Wine u 9producedIn.france
Unsound!!
SpanishWine Wine u 9producedIn.Spain FrenchWine
Wine u 9producedIn.France France u Spain ?
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Pseudo-nominals unsoundness

• Suppose we define the concept of a wine that is
  produced in at least three different countries
• Wine u 3 producedIn.Country
• Suppose I have only two countries in my ontology
• Country Spain,France
• My concept is then unsatisfiable.
• Suppose we now use pseudo-nominals and treat
  Spain and France as disjoint atomic concepts.
  Then, our concept is satisfiable.