Understanding Real-world Ontologies

1. Understanding Real-world Ontologies

2. 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.

3. Analysis of Real-world Ontologies

4. 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.

5. 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

6. 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!

7. 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 nameAto a complex concept C Some examples: LungPathology ´ Pathology u9 locativeAttribute.Lung RenalTransplant ´ Transplanting u9 actsOn.Kindney

8. 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 v9 hasFunction.AerobicMetabolicProcess • In GALEN, some of these can be very complex: • check out the definitions of Knee Joint and Kidney!

9. 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 u9 actsSpecificallyOn.Leucocidin v 9 isFunctionOf.StraphilococcusAureus 9 actsOn.Glucose u Transport u9 carriesFrom.Blood v 9 carriesTo.Cell

10. 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

11. 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!

12. 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

13. 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!

14. 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

15. 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

16. 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!

17. Axioms in NCI Examples: Cancer_Gene v Gene u9 hasFunction.Tumoregenesis Alzheimer_Disease v Dementia Domain(anatomic_Structure_has_Location) = Anatomy_Kind Range(technique_hasPurpose) = Clinical_Or_Research_Activity_Kind

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

19. 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

20. 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

21. 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.

22. 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

23. Tambis: the (unclassified) hierarchies • Subclass relationships: 226 • No multiple inheritance • Maximum depth of class tree: 6 • Maximum depth of property tree: 2

24. 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

25. 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!

26. Tambis: Unsatisfiable concepts • Almost half of the concepts in Tambis are unsatisfiable • The explanations are non-trivial • Check outprotein-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

27. Tambis: Repair

28. Advanced Issues and Design Patterns

29. Qualified Number Restrictions (QCRs) • Existential restrictions in OWL DL are qualified: • Person u9hasChild.Male • Cardinality restrictions can only be qualified with > • Person u9hasChild.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.

30. Qualified Cardinality Restrictions Can be approximated using property inclusion and property range. Quadruped ´ Animal u (= 4 hasLeg) hasLeg v hasPart Range(hasLeg) = Leg

31. 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

32. 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.

33. 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?

34. 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 v9hasIdol.{elvis}

35. 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)

36. Faking Nominals Pseudonominals: Approximation to nominals SpanishWine = Wine u9producedIn.{spain} FrenchWine = Wine u9producedIn.{france} Unsound!! SpanishWine = Wine u9producedIn.Spain FrenchWine = Wine u9producedIn.France France u Spain = ?

37. 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.