CO-ODE/HyOntUse JISC/EPSRC 1 Why I need both OWL/DLs & Frames Alan Rector Medical Informatics Group Bio Health Informatics Forum Department of Computer Science University of Manchester oiled.man.ac.uk

CO-ODE/HyOntUse JISC/EPSRC 2 CO-ODE/HyOntUse Bringing Protégé and OWL/OilEd Together OilEd The de facto standard editor for DAML+OIL/OWL/ logic-based ontologies Protégé: The de facto standard environment for frames Plus methods from OpenGALEN PEN&PAD & AKT Not as easy as it looks!

CO-ODE/HyOntUse JISC/EPSRC 3 Very Brief History of OWL OIL – European approach – Description Logics in Frame Clothing –Initial OilEd - Manchester DAML – DARPA Agent Markup Language – DARPA DAML+OIL –First joined up approach- EU+DARPA OWL –Emerging W3C WebOnt Standard 3 Flavours – Lite, DL, and Full – & still evolving –I work mostly with the subset of DL that works with existing classifiers De facto standard way to apply logic-based ontologies – OilEd still the main editor but new efforts e.g. PROTÉGÉ-OilEd/OWL tab coming

CO-ODE/HyOntUse JISC/EPSRC 4 Why I need both OWL/DLs and Frames Build real large-scale knowledge intensive applications “Ontology Anchored Knowledge Bases” –Fractal Adaptation “Rebuild PEN&PAD introduction” –GRAIL is essentially a hybrid Frame/DL system Build robust auditable applications –Get the ontology right –Meta data and provenance Achieve sufficient abstraction for re-use –From application ontologies to domain ontologies Get the right answer to the intended question –Do I mean “Is it possible” or “Is it true”? –Do only what is needed

CO-ODE/HyOntUse JISC/EPSRC 5 Specific Information on Individuals Data store Why I need both OWL/DLs & Frames To Build Knowledge Intensive applications –Knowledge bases anchored on ontologies supporting information resources –Meta data with everything Contingent Knowledge Knowledge Base Necessary knowledge Ontology Meta Data Annotation

CO-ODE/HyOntUse JISC/EPSRC 6 Why I need OWL/DLs Maintain large, complex ontologies/terminologies –Parsimonious ontologies - “Conceptual lego” Avoid combinatorial explosions –Strong semantics for Reasoning about Subsumption & Normalisation Modularity Avoid inheritance conflicts (“Nixon Diamonds”) …but it lacks –Meta data –Defaults & exceptions –Reflective queries –Reasoning/Querying with individuals –Other forms of reasoning – arithmetic, coordinate/unit transformation, … …and it does too much –Complete reasoning about what is possible when I need predictable reasoning about what is true Domain & range checks

CO-ODE/HyOntUse JISC/EPSRC 7 Why I need Frames/Protégé Manage Metadata, Contingent knowledge & Individuals –Knowledge about Knowledge –Defaults & exceptions – classic frame reasoning –Individuals –Reflective queries – ask about the knowledge base itself Hybrid reasoning –Easy to integrate special purpose solutions for special purpose problems –Easy to extend expressiveness for queries …but it lacks –Parsimonious representation – No “Lego” –Strong semantics for subsumption Reasoning about what is possible rather than just what is

CO-ODE/HyOntUse JISC/EPSRC 8 I need to experiment with much more metadata “Provenance, provenance, provenance”

CO-ODE/HyOntUse JISC/EPSRC 9 Maintaining large 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” OpenGALEN & OWL

CO-ODE/HyOntUse JISC/EPSRC 10 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 Frames OWL / DLs –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  Meta data  Contingent Knowledge Defaults & Exceptions  Reflective queries  Individuals  Hybrid reasoning

CO-ODE/HyOntUse JISC/EPSRC 11 Encrustation + involves: MitralValve Thing + feature: pathological Structure + feature: pathological + involves: Heart OWL/Logic Based Ontologies: The basics Thing Structure HeartMitralValveEncrustation MitralValve * ALWAYS partOf: Heart Encrustation * ALWAYS feature: pathological Feature pathological red + (feature: pathological) red + partOf: Heart red + partOf: Heart PrimitivesDescriptionsDefinitionsReasoningValidating

CO-ODE/HyOntUse JISC/EPSRC 12 The Key: Normalising (untangling) Ontologies Structure Function Part-whole Structure Function Part-whole

CO-ODE/HyOntUse JISC/EPSRC 13 The Key: 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 Insulin  playsRole HormoneRole...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’ … ActionRole PhysiologicRole HormoneRole CatalystRole … … Substance BodySubstance Protein Insulin Steroid …

CO-ODE/HyOntUse JISC/EPSRC 14 The benefits Avoiding combinatorial explosions The “Exploding Bicycle” From “phrase book” to “dictionary + grammar” – ICD-9 (E826) 8 – READ-2 (T30..) 81 – READ-3 87 – 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

CO-ODE/HyOntUse JISC/EPSRC 15 The benefits: Modularisation Bridging Scales and context with Ontologies Genes Species Protein Function Disease Protein coded by gene in humans Function of Protein coded by gene in humans Disease caused by abnormality in Function of Protein coded by gene in humans Gene in humans

CO-ODE/HyOntUse JISC/EPSRC 16 Benefits: Fractal Indexing on multiple axes Indefinite customisation from a finite knowledge base Consistent application of policies –“Fail soft” – always produce something plausible Multiple axes of specialisation Normalised ontologies produce few inheritance conflicts –Condition –Use case Task User type Setting –Medium Browser, PDA, WAP, Thick client, …

CO-ODE/HyOntUse JISC/EPSRC 17 Example: Fractal tailoring of Forms/Guidelines/Procedures Cough –Initial evaluation in general practice –routine evaluation in general practice routine evaluation by nurse in general practice –Home monitoring Cough in patient with TB –as above –In chest clinic In Dr Jones’ chest clinic –In Dr Jones’ chest clinic seen by a trainee 100 diseases x 10 complications x 5 settings x 5 user types x 5 tasks  situations Do you really want to enumerate them by hand? maintain them?

CO-ODE/HyOntUse JISC/EPSRC 18 PEN&PAD Fractal Tailoring of ‘fail soft’ forms

CO-ODE/HyOntUse JISC/EPSRC 19

CO-ODE/HyOntUse JISC/EPSRC 20 Idiopathic Hypertension in our co’s Phase 2 study Fractal tailoring forms for clinical trials Hypertension Idiopathic Hypertension In our company’s studies In Phase 2 studies Hypertension Idiopathic Hypertension` In our company’s studies In Phase 2 studies

CO-ODE/HyOntUse JISC/EPSRC 21 Other Fractal Indexing Tasks Mapping to between coding systems and ontologies –From logical to alogical systems – e.g. ICD10 All ICD “excludes” come automatically –Drug interactions and contraindications and usage Contingent knowledge – not part of necessary nature of drug –Help systems Gather all relevant information from all levels –Selecting relevant guidelines and trial protocols

CO-ODE/HyOntUse JISC/EPSRC 22 But it is not trivial OWL and Frame paradigms are more different than they look –OWL is concerned with axioms –Protégé is concerned with facts Structure of graph –OWL focuses on restrictions Roughly the allowed classes/existent classes facets Class-instance distinction principled … –Protégé focuses on values Meaning of a class value ambiguous – used differently in different applications Class instance distinction application dependent … OWL supports ONLY an ontology & one kind of reasoning –Protégé supports knowledge bases & potentially many kinds of querying but not OWL’s open world reasoning!

CO-ODE/HyOntUse JISC/EPSRC 23 Classifying and Querying Only doing the reasoning necessary Classifying – OWL, DLs, … –What must be true or false axiomsIn any extension of this “world” consistent with axioms –related to modal logics –Negation = impossibility (“unsatisfiability”) “Open World” –Computationally expensive Limits expressivity Persistent Querying – PAL, Query tab, SQL, … –What is true or false factsIn this “world” about which we know facts –Negation = failure “Closed World” –Computationally relatively cheap (usually) Ephemeral

CO-ODE/HyOntUse JISC/EPSRC 24 Classifying and Querying: The Pizza Example MyPizza == Pizza hasTopping Peppers hasTopping Mushrooms Is MyPizza a vegetarian pizza? –Classification/OWL: “No” – not necessarily, you haven’t said it doesn’t have meat –Negation as impossibility »open world –Querying/Database “Yes” – I can’t find any meat –Negation as failure »closed world

CO-ODE/HyOntUse JISC/EPSRC 25 User Oriented Ontology Development CO-ODE & HyOntUse New projects under the UK JISC/EPSRC joint initiative on Semantic Web & Autonomic Computing Initiative parallel with US NLM/NCI funding –Collaboration - Manchester, Stanford, Southampton/Epistemics Integrate – Bridge the Gaps –Frames & Metaknowledge – Protégé Plug & Play environment –Visualisation, DAGs, Constraints, … –Logic based domain ontologies – DAML+OIL/OWL/OilEd User oriented debugging and visualisation –Views & Perspectives – GALEN –User oriented design / Knowledge Elicitation –AKT/Southampton

CO-ODE/HyOntUse JISC/EPSRC 26 CO-ODE/HyOntUse Consortium Manchester CS –With thanks to Sean Bechhofer, Carole Goble, … Stanford Medical Informatics –With thanks to Holger Knublauch, Ray Fergerson, … Southampton Advanced Knowledge Technologies –With thanks to Nigel Shadbolt & Clive Embury (Epistemics) …and all of you –Help to improve usability, visualisation, applications, …, … Help us – Help yourselves – Join in – Invite others!Help us – Help yourselves – Join in – Invite others! PS – Post Doc Needed!