8-jan-16 combining the strengths of umist and the victoria university of manchester understanding...
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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
Understanding Real-world Ontologies
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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
Analysis of Real-world Ontologies
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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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.Lung
RenalTransplant ´ Transplanting u 9 actsOn.Kindney
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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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 don’t succeed use the pinpointing explanation service
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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
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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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
Tambis: Repair
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Advanced Issues and Design Patterns
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Apr 21, 2023Combining the strengths of UMIST andThe Victoria University of Manchester
Qualified Number Restrictions (QCRs)• Existential restrictions in OWL DL are qualified:
– Person u 9hasChild.Male
• Cardinality restrictions can only be qualified with >– 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 RestrictionsThis 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 hasMemberhasNotMDMember v hasMemberRange(hasMDMember) = MDRange(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
– owl:oneOf, owl:hasValue
• owl:oneOf - Enumeration of individuals.– WineColor {red, white, rose}
• {red, white, rose} = {red} t {white} t {rose}
• owl:hasValue - 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 NominalsPseudonominals: Approximation to nominals
SpanishWine = Wine u 9producedIn.{spain}
FrenchWine = Wine u 9producedIn.{france}
SpanishWine = Wine u 9producedIn.Spain
FrenchWine = Wine u 9producedIn.France
France u Spain = ?
Unsound!!
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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.