Ontology at Manchester

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<ul><li>1.Ontology at Manchester Robert Stevens BioHealth Informatics Group School of Computer Science University of Manchester </li></ul> <p>2. 2 Ontology Research at Manchester Language and Reasoning Tools Modelling 3. 3 So what is an ontology? Catalog/ ID Thesauri Terms/ glossary Informal Is-a Formal Is-a Formal instance Frames (properties) General Logical constraints Value restrictions Disjointness, Inverse, partof Gene Ontology Mouse Anatomy EcoCyc PharmGKB TAMBIS Arom After Chris Welty et al 4. 4 A Definition o a set of logical axioms designed to account for the intended meaning of a formal vocabulary used to describe a certain (conceptualisation of) reality [Guarino 1998] o conceptualisation of inserted by me o Logical axioms means a formal definition of meaning of terms in a formal language o Formal languagesomething a computer an reason with o Use symbols to make inferences o Symbols represent things and their relationships o Making inferences about things computationally amenable 5. 5 OWL Ontologies will form the back bone of the semantic web OWL is the latest standard in ontology languages from the W3C Layered on top of RDF and RDF Schema Underpinned by Description Logics 6. 6 OWL represents classes of instances A B C 7. 7 Interpretations Individuals are interpreted as objects Classes are interpreted as sets containing objects Properties are interpreted as binary relations on objects 8. 8 Logical Descriptions Class: Water EquivalentTo: Molecule that madeOf 1 OxygenAtom and madeOf 2 HydrogenAtom and madeOf only (OxygenAtom or HydrogenAtom) Class: WaterSubClassOf: Molecule that hasBoilingPoint value 100 and hasFreezingPoint value 0 and hasState some Liquid 9. 9 Reasoning These OWL descriptions can be submitted to a DL reasoner Translated into DL Checked for consistencyis what weve said satisfiable Also infers subsumption hierarchy implied by statements Mistakes all too easy without help Formality is your friend 10. 10 Language &amp; Reasoning Supporting ontology engineering by automated reasoning Classification Consistency checking Query answering Say the things you want to say and still reason Explain reasoning results Help debugging unexpected results Supporting modularity in ontologies Segmenting large ontologies into modules 11. 11 Language &amp; Reasoning Inspecting ontologies to find missing knowledge Scalability: Larger ontologies; faster reasoning; more instances; more expressivity Instance Store: Query answering over vast numbers of instances 12. Old Protg (matrix wizard) 13. New Protg (matrix tab) 14. SWOOP (crop circles) 15. 15 ComparaGRID 16. 16 Classsifying Protein Phosphatases Annotating a genomes proteins is a bottleneck Classifying proteins is a first step to annotation Tools for detecting features Need human knowledge to determine class membership Can we capture how to recognise a phosphatase in an ontology? 17. 17 Definition of Tyrosine Phosphatase Class: TyrosinePhosphatase Complete (Protein and - (contains atLeast-1 ProteinTyrosinePhosphataseDomain) and - (contains 1 TransmembraneDomain)) 18. 18 Definition for R2A Phosphatase Class R2A Complete (Protein and - (contains 2 ProteinTyrosinePhosphataseDomain) and - (contains 1 TransmembraneDomain )and - (contains 4 FibronectinDomains) and - (contains 1 ImmunoglobulinDomain) and - (contains 1 MAMDomain) and - (contains 1 Cadherin-LikeDomain) and - (contains only (TyrosinePhosphataseDomain or TransmembraneDomain or FibronectinDomain or ImnunoglobulinDomain or Clathrin-LikeDomain or ManDomain))) 19. 19 Building the Ontology Classifications already made by biologists based on protein functionality; Protein domain composition and other details in the literature; Some 50 classes of phosphatase, 30 protein domains and 39 relationships; Value partition of protein domains (covering and disjoint); Defines range of contains property; Literature contains knowledge of how to recognise members of each class of phosphatase. 20. 20 Incremental Addition of Protein Functional Domains Phosphatase catalytic Cadherin-like Immunoglobulin MAM domain Cellular retinaldehyde Adhesion recognition Transmembrane Fibronectin III Glycosylation 21. 21 Classification of the Classical Tyrosine Phosphatases 22. 22 What is the Ontology Telling Us? Each class of phosphatase defined in terms of domain composition We know the characteristics by which an individual protein can be recognised to be a member of a particular class of phosphatase We have this knowledge in a computational form If we had protein instances described in terms of the ontology, we could classify those individual proteins A catalogue of phosphatases 23. 23 Classification of Protein Tyrosine Phosphatases 24. 24 Results Human gold standard: Same results plus two more Partially annotated A. fumigatis: Better results and two new putative phosphatases Easily generated and compared phosphatase profiles Parasites Whole range of unexpected results---back to bioinformatics sequence analysis 25. 25 myGrid Service Ontology myGrid services and workflow toolkit Web service discovery and composition Semantic content of provenance repository Wide use of service ontology Links wit BioMOBY Workflows as knowledge management 26. 26 Informal Modelling OWL is formal, but ontology has a long informal stage Tool forms of knowledge elicitation techniques such as card sorting and laddering Experiments with text to ontology tools With suitable text can truncate the informal stage Provide useful starting points for later stages 27. 27 Casual Modelling OWL can be scary Need the equivalent of pseudo-code Work on concept maps as an elicitation tool Convertible to OWL Converting spreadsheets to OWL Converting thesaurae to OWL 28. 28 Community Building of Ontologies Collaboration with University of British Columbia, Vancouver No money and no centre: What can you do? Use your community to build, extend, check facts in your ontology Currently running experiments 29. 29 The Sealife Browser An EU project to build a Semantic Grid browser for the life sciences Uses ontology as background knowledge Dynamically link to terms on a page Link to tools, data, documents, etc A semantic shopping cart Need to use a broad range of ontologies and many conversions 30. 30 Modelling Biology &amp; Medicine Describing biological phenomena Reconciling descriptions Analysing biological data Describing and analysing healthcare records Guiding annotation: Creating and filling forms Describing medical phenomena 31. 31 Outside Relationships BioPAX FUGO/OBI Plant Ontology CBIO HL7, 32. 32 Training Introductory OWL tutorials: Non- biological Advanced tutorial: Biology orientated Hundreds trained in UK and overseas (mainly life sciences) Hands-on training </p>