Scale and Context: Issues in Ontologies to link Health- and Bio-Informatics

1. Scale and Context: Issues in Ontologies to link Health- and Bio-Informatics Alan Rector, Jeremy Rogers, Angus Roberts, Chris WroeBio and Health Informatics Forum/Medical Informatics GroupDepartment of Computer Science, University of Manchester rector@cs.man.ac.ukwww.cs.man.ac.uk/mig img.man.ac.ukwww.clinical-escience.orgwww.opengalen.org

2. Organisation of Talk • Informal presentation, motivation & examples • Intro to logic based ontologies • How to use logic based ontologies to represent scales and context • Making context modular – normalisation • Recurrent distinctions • and tests for those distinctions • Summary

3. Example Problems of Context • Classification by multiple axes • e.g. Molecular action, physiologic, and pathological effects • Chloride transport & Cystic fibrosis • Biological Scope • eg. Normal/Abnormal, Human/Mouse • Conceptual view • e.g. the Digital Anatomist Foundational Model of organs vs Clinical convention –Is the pericardium a part of the heart?

4. Basic Approach • Separate information into independent modules • Normalise the ontology • Represent knowledge cleanly • Add explicit contextual information • Don’t distort the clean structure • Add context to it explicitly

5. Why use Logic-based Ontologies? becauseKnowledge is Fractal! &Changeable! &Contextual!

6. hand extremity body Lung inflammation infection abnormal normal Logic-based Ontologies: Conceptual Lego gene protein cell expression chronic acute bacterial deletion polymorphism ischaemic

7. Logic-based Ontologies: Conceptual Lego “SNPolymorphism of CFTRGene causing Defect in MembraneTransport of ChlorideIon causing Increase in Viscosity of Mucus in CysticFibrosis…” “Hand which isanatomicallynormal”

8. Logic based ontologies • A formalisation of semantic nets, frame systems, and object hierarchies via KL-ONE and KRL • “is-kind-of” = “implies” (“logical subsumption”) • “Dog is a kind of wolf” means“All dogs are wolves” • Modern examples: DAML+OIL /“OWL”?) • Older variants LOOM, CLASSIC, BACK, GRAIL, K-REP, …

9. Feature Structure Thing + feature: pathological red pathological Heart MitralValve MitralValve * ALWAYS partOf: Heart Encrustation * ALWAYS feature: pathological Encrustation Structure + feature: pathological + involves: Heart Encrustation + involves: MitralValve Logic Based Ontologies: The basics Primitives Descriptions Definitions Reasoning Validating Thing red + partOf: Heart red + partOf: Heart + (feature: pathological)

10. Bridging Bio and Health Informatics • Define concepts with ‘pieces’ from different scales and disciplines and then combine them • “Polymorphism which causes defect which causes disease” • Use concepts which make context explicit • “ ‘Hand which is anatomically normal’  has five fingers” • Use different subproperties for different contexts • “Abnormalities of clinical parts of the heart”

11. Protein CFTRGene in humans Membrane transport mediated by (Protein coded by (CFTRgene in humans)) Protein coded by(CFTRgene & in humans) Disease caused by (abnormality in (Membrane transport mediated by (Protein coded by (CTFR gene & in humans)))) Bridging Scales with Ontologies Species Genes Function Disease

12. Use composition to express context • Normal and abnormal Hand  isSubdivisionOf some UpperExtremity Hand & AnatomicallyNormal  hasSubdivision exactly-5 fingers • Homologies and Orthologies Thumb of Hand of Human  hasFeature Opposable Thumb of Hand of NonHumanPrimate ¬hasFeature Opposable

13. Disease of part_of Heart is_part_of OrganPart Organ Heart CardiacValve Pericardium Disease of Pericardium is_clinically_part_of Represent context and views by variant properties is_structurally_part_of

14. Context can give combinatorial explosions • Avoid the “Exploding Bicycle”From “phrase book” to “dictionary + grammar” • 1980 - ICD-9 (E826) 8 • 1990 - READ-2 (T30..) 81 • 1995 - READ-3 87 • 1996 - ICD-10 (V10-19 Australian) 587 • V31.22 Occupant of three-wheeled motor vehicle injured in collision with pedal cycle, person on outside of vehicle, nontraffic accident, while working for income • and meanwhile elsewhere in ICD-10 • W65.40 Drowning and submersion while in bath-tub, street and highway, while engaged in sports activity • X35.44 Victim of volcanic eruption, street and highway, while resting, sleeping, eating or engaging in other vital activities

15. Structure Function Structure Part-whole Part-whole Function The Cost 1: Normalising (untangling) Ontologies

16. … ActionRole PhysiologicRole HormoneRole CatalystRole … … Substance BodySubstance Protein Insulin Steroid … The Cost 1: Normalising (untangling) OntologiesMaking each meaning explicit and separate PhysSubstance Protein ProteinHormone Insulin Enzyme Steroid SteroidHormone Hormone ProteinHormone^ Insulin^ SteroidHormone^ Catalyst Enzyme^ PhysSubstance Protein‘ ProteinHormone’ Insulin‘Enzyme’ Steroid‘SteroidHormone’ ‘Hormone’ ‘ProteinHormone’ Insulin^‘SteroidHormone’ ‘Catalyst’‘Enzyme’ ...and helping keep argument rational and meetings short! Hormone = Substance & playsRole-HormoneRole ProteinHormone = Protein & playsRole-HormoneRoleSteroidHormone = Steroid & playsRole-HormoneRole Catalyst = Substance & playsRole CatalystRole Insulin playsRole HormoneRole Enzyme ?=? Protein & playsRole-CatalystRole

17. The Cost: 2 – Clean Distinctions & Tests • Repeating patterns within levels • Structures vs Substances • Flavours of part-whole • Part-whole vs containment, connection, branching • Process/Event vs Thing (“Endurant” vs “Perdurant”) • … • Repeating patterns across levels • Multiples at one level act as substances at the next • Substances span levels; structures are specific to a level

18. Repeating Patterns within each level • Structures vs Substances (Discrete vs Mass) • Structures are made of substances • Organs are made of tissue • Parts & portions • Structures have parts & subdivisions,… • Substances have portions • Portions can have proportions & concentrations

19. Tests • Structures (Discrete) • Can you count it? Is one part different from another? Is it made of something(s)? • Books, organs, ideas, individual cells, organisations, … • Substance (Mass) • Are all bits the same? Can something be made of it? Can you talk about “A piece of it”? “A lump of it”? “A stream of it”? … • Water, sodium, tissue, blood, …

20. Repeating Patterns within each level • Part-whole vs containment • Parthood is organisational • The wall is part of the cell; • cornea is part of the eye • Containment is physical • The inclusion is contained in the cell • The marrow is contained in the bone • Often occur together • Nucleus is a part of and contained in the cell • The retina is part of and contained in the eye

21. Tests • Parts • If I take the part away, is the whole incomplete? • If the part is damaged is the whole damaged? • If I do something to the part do I do something to the whole? • Containment • Is the contained thing inside the container? • Is the relationship spatial/physical? (or temporal?)

22. Repeating Patterns bridging levels • Multiples of structures at one level behave as substances at the next • “Blood is made of in part a multiple of red cells”“Tissue is made of in part a multipleof cells”“A rash is a multiple of spots”“Polyposis is a multiple of polyps”“A flock is a multiple of birds” • Multiples are not Sets • Note defined by members • Membership can change (intensional rather than extensional) • Action on the singleton is not action on the multiple;Action on the whole is (usually) action on the singletons • If I treat a spot, I do not treat the rash • If I treat the rash, I treat the spots

23. Tests • Multiples • Name for the singleton – “grain”, “cell”, “bird”? • Singletons are countable? • Multiple is measurable rather than countable? • Odd to say part-of “This cell is part of the Arm”?

24. Be normalised SummaryLet the logic engine do the work • Logic based ontologies can bridge granularities & represent context explicitly • And manage the potential combinatorial explosions • To do so • Structure must • Make all relevant information explicit • Be modular • Conception must • Make ontological distinctions cleanly • Parts, wholes, containment, structures, substances, … • Developers need systematic tests for each distinction