Manchester Medical Informatics Group OpenGALEN 1 Linking Formal Ontologies: Scale, Granularity and Context Alan Rector Medical Informatics Group, University of Manchester img.cs.man.ac.uk

Manchester Medical Informatics Group OpenGALEN 2 Why use Logic-based Ontologies? because Knowledge is Fractal! & Changeable!

Manchester Medical Informatics Group OpenGALEN 3 Four Roles of Terminology/Ontologies Content of Databases and Patient Records –Structural linkage within EPR/EHR & messages –Content of EPR/EHR & messages Capturing information - the user interface Linkage between domainsLinkage between domains –Health and Bio Sciences –Macro, Micro, and Molecular scales –Contexts: Normal / abnormal; species; stage of development –Healthcare delivery and Clinical research –Patient Records and Decision Support Indexing Information –Metadata and the semantic web

Manchester Medical Informatics Group OpenGALEN 4 Logic based ontologies The descendants of frame systems and object hierarchies via KL-ONE “is-kind-of” = “implies” –“Dog is a kind of wolf” means “All dogs are wolves” –Therefore logically computable Modern examples: OIL, DAML+OIL (“OWL”?) –Underpinned by the FaCT family of Description Logic Reasoners Others LOOM, CLASSIC, BACK, GRAIL,

Manchester Medical Informatics Group OpenGALEN 5 Logic-based Ontologies: Conceptual Lego hand extremity body acute chronic abnormal normal ischaemic deletion bacterial polymorphism cell protein gene infection inflammation Lung expression

Manchester Medical Informatics Group OpenGALEN 6 Logic-based Ontologies: Conceptual Lego “ SNPolymorphism of CFTRGene causing Defect in MembraneTransport of ChlorideIon causing Increase in Viscosity of Mucus in CysticFibrosis …” “Hand which is anatomically normal”

Manchester Medical Informatics Group OpenGALEN 7 What’s in a “Logic based ontology”? Primitive concepts - in a hierarchy –Described but not defined Properties - relations between concepts –Also in a hierarchy Descriptors - property-concept pairs –qualified by “some”, “only”, “at least”, “at most” Defined concepts –Made from primitive concepts and descriptors Axioms –disjointness, further description of defined concepts A Reasoner –to organise it for you

Manchester Medical Informatics Group OpenGALEN 8 Encrustation + involves: MitralValve Thing + feature: pathological Structure + feature: pathological + involves: Heart Logic Based Ontologies: A crash course Thing Structure HeartMitralValveEncrustation MitralValve * ALWAYS partOf: Heart Encrustation * ALWAYS feature: pathological Feature pathological red + (feature: pathological) red + partOf: Heart red + partOf: Heart

Manchester Medical Informatics Group OpenGALEN 9 Bridging Bio and Health Informatics Define concepts with ‘pieces’ from different scales and disciplines –“Polymorphism which causes defect which causes disease” Define concepts which make context explicit –“ ‘Hand which is anatomically normal’  has five fingers” Separate properties for different contexts/views –“Abnormalities of clinical parts of the heart” includes pericardium

Manchester Medical Informatics Group OpenGALEN 10 Bridging Scales and context with Ontologies Genes Species Protein Function Disease Protein coded by gene in species Function of Protein coded by gene in species Disease caused by abnormality in Function of Protein coded by gene in species Gene in Species

Manchester Medical Informatics Group OpenGALEN 11 Representing context and views by variant properties Organ Heart Pericardium OrganPart CardiacValve Disease of (is_part_of) Heart Disease of Pericardium is_part_of is_structurally_part_ofis_clinically_part_of

Manchester Medical Informatics Group OpenGALEN 12 The cost: Ontologies are not Thesauri A Mixed Hierarchy Works for navigation by humans Works for “Disease of…’ and ‘Procedure on…’ Fails for “Surface of…” How can the computer know the difference?

Manchester Medical Informatics Group OpenGALEN 13 From a thesaurus to a logic-based ontology A logic-based is-kind-of (subsumption) hierarchy Untangle part-whole and is-kind-of in anatomic ontology Link Clinical Ontology with Anatomical ontology Add rule that “Disorder of part  disorder of whole” Reasoner can then create automatically:

Manchester Medical Informatics Group OpenGALEN 14 Examples common in Bio Ontologies Is part of Golgi membrane Integral protein Is part of Plasma membrane Apical plasma membrane

Manchester Medical Informatics Group OpenGALEN 15 The Cost: Normalising (untangling) Ontologies Structure Function Part-whole Structure Function Part-whole

Manchester Medical Informatics Group OpenGALEN 16 The Cost: Normalising (untangling) Ontologies Making each meaning explicit and separate PhysSubstance Protein ProteinHormone Insulin Enzyme Steroid SteroidHormone Hormone ProteinHormone^ Insulin^ SteroidHormone^ Catalyst Enzyme^ Hormone = Substance & playsRole-HormoneRole ProteinHormone = Protein & playsRole-HormoneRole SteroidHormone = Steroid & playsRole-HormoneRole Catalyst =Substance & playsRole CatalystRole...and helping keep argument rational and meetings short! Enzyme ?=? Protein & playsRole-CatalystRole PhysSubstance Protein ‘ ProteinHormone’ Insulin ‘Enzyme’ Steroid ‘SteroidHormone’ ‘Hormone’ ‘ProteinHormone’ Insulin^ ‘SteroidHormone’ ‘Catalyst’ ‘Enzyme’

Manchester Medical Informatics Group OpenGALEN 17 The Cost You can’t say everything you want to –Expressiveness costs computational complexity More inference takes more time –Scaling for complex tasks still being investigated Many other kinds of reasoning needed It doesn’t make the ! Coffee!

Manchester Medical Informatics Group OpenGALEN 18 Other benefits Limit combinatorial explosions From “phrase book” to “dictionary + grammar” Avoid the “exploding bicycle” – ICD-9 (E826) 8 – READ-2 (T30..) 81 – READ-3 87 – ICD-10 (V10-19) 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

Manchester Medical Informatics Group OpenGALEN 19 Study a phase 2 Other benefits Hypertension Idiopathic Hypertension In our company’s studies Study a Phase 2 Hypertension Idiopathic Hypertension` In our company’s studies Phase 2 Index and assemble information

Manchester Medical Informatics Group OpenGALEN 20 Summary: Logic based ontologies because Knowledge is Fractal Link “Conceptual Lego” –at all levels indefinitely –Spanning scales, genotype, phenotype, etc. Model context and views –Express differences explicitly Manage combinatorial explosion Index information efficiently Next step: Larger scale demonstrations in Genotype to Phenotype