1 / 20

Linking Formal Ontologies: Scale, Granularity and Context

Linking Formal Ontologies: Scale, Granularity and Context. Alan Rector Medical Informatics Group, University of Manchester www.cs.man.ac.uk/mig www.opengalen.org img.cs.man.ac.uk rector@cs.man.ac.uk. Why use Logic-based Ontologies?. because Knowledge is Fractal!. & Changeable!.

pfinch
Download Presentation

Linking Formal Ontologies: Scale, Granularity and Context

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Linking Formal Ontologies: Scale, Granularity and Context Alan Rector Medical Informatics Group, University of Manchesterwww.cs.man.ac.uk/migwww.opengalen.orgimg.cs.man.ac.ukrector@cs.man.ac.uk

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

  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 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 • www.semanticweb.org www.w3c.org

  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,... • www.ontoknowledge.org/oil www.semanticweb.org

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

  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 isanatomicallynormal”

  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 • AReasoner • to organise it for you

  8. 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: A crash course Thing red + partOf: Heart red + partOf: Heart + (feature: pathological)

  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

  10. Protein Gene in Species Disease caused by abnormality inFunction ofProtein coded bygene in species Protein coded bygene in species Function ofProtein coded bygene in species Bridging Scales and context with Ontologies Species Genes Function Disease

  11. Disease of (is_part_of) Heart Organ OrganPart is_clinically_part_of is_structurally_part_of Heart CardiacValve Pericardium Disease of Pericardium Representing context and views by variant properties is_part_of

  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?

  13. A logic-based is-kind-of (subsumption) hierarchy From a thesaurus to a logic-based ontology 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:

  14. Golgi membrane Integral protein Is part of Is part of Plasma membrane Apical plasma membrane Examples common in Bio Ontologies

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

  16. The Cost: 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 Enzyme ?=? Protein & playsRole-CatalystRole

  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 Itdoesn’t make the! Coffee!

  18. Other benefits • Limit combinatorial explosionsFrom “phrase book” to “dictionary + grammar” Avoid the “exploding bicycle” • 1980 - ICD-9 (E826) 8 • 1990 - READ-2 (T30..) 81 • 1995 - READ-3 87 • 1996 - 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

  19. Study a phase 2 Other benefits • Index and assemble information Hypertension Hypertension Idiopathic Hypertension Idiopathic Hypertension` In our company’s studies In our company’s studies Study a Phase 2 Phase 2

  20. Summary: Logic based ontologies becauseKnowledge 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

More Related