the ontology of experiments n.
Skip this Video
Loading SlideShow in 5 Seconds..
The Ontology of Experiments PowerPoint Presentation
Download Presentation
The Ontology of Experiments

Loading in 2 Seconds...

play fullscreen
1 / 99

The Ontology of Experiments - PowerPoint PPT Presentation

  • Uploaded on

The Ontology of Experiments. Barry Smith Plan. The Experiment Ontology The Ontology of Biomedical Investigations Unit Ontology Phenotype Ontology Document Ontology. EXPO. The Ontology of Experiments L. Soldatova, R. King

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

The Ontology of Experiments

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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
    Presentation Transcript
    1. The Ontology of Experiments Barry Smith

    2. Plan • The Experiment Ontology • The Ontology of Biomedical Investigations • Unit Ontology • Phenotype Ontology • Document Ontology

    3. EXPO • The Ontology of Experiments • L. Soldatova, R. King • Department of Computer Science • The University of Wales, Aberystwyth

    4. EXPO • controlled vocabulary; • meta-model; • theory of content; • knowledge management • 􀂄􀂄 knowledge systematization; • 􀂄􀂄 knowledge sharing; • 􀂄􀂄 knowledge treatment; • 􀂄􀂄 knowledge reusability; • data integration.

    5. EXPO Formalisation of Science • The goal of science is to increase our knowledge of the natural world through the performance of experiments. • This knowledge should, ideally, be expressed in a formal logical language. • Formal languages promote semantic clarity, which in turn supports the free exchange of scientific knowledge and simplifies scientific reasoning.

    6. Adam Pease Suggested Upper Merged Ontology

    7. SUMO top level • Entity • Physical • Object • SelfConnectedObject • Substance • CorpuscularObject • Food • Region • Collection • Agent • Process • Abstract • SetOrClass • Relation • Quantity • Number • PhysicalQuantity • Attribute • Proposition

    8. Suggested Upper Merged Ontology • 1000 terms, 4000 axioms, 750 rules • Associated domain ontologies totalling 20,000 terms and 60,000 axioms • [includes ontology of boundaries from BS]

    9. Structural Ontology Base Ontology Set/Class Theory Numeric Temporal Mereotopology Graph Measure Processes Objects Qualities SUMO Structure

    10. Structural Ontology SUMO Base Ontology Set/Class Theory Numeric Temporal Mereotopology Graph Measure Processes Objects Qualities Mid-Level WMD Transnational Issues Financial Ontology Geography ECommerce Services Communications Distributed Computing Government People Military Terrorist Attack Types Terrorist Transportation Economy Biological Viruses Terrorist Attacks UnitedStates Elements NAICS Afghanistan France World Airports … SUMO+Domain Ontology Total Terms Total Axioms Rules 20399 67108 2500

    11. entityphysicalobjectprocessdual object processintentional processintentional psychological processrecreation or exerciseorganizational processguidingkeepingmaintainingrepairingpokingcontent developmentmakingconstructingmanufacturepublicationcookingsearchingsocial interactionmaneuvermotioninternal changeshape changeabstract

    12. corpuscular object =def. A SelfConnectedObject whose parts have properties that are not shared by the whole. Subclass(es) organic object artifact Coordinate term(s) content bearing object food substance Axiom: corpuscular object is disjoint from substance. substance =def. An Object in which every part is similar to every other in every relevant respect.

    13. advantages of SUMO • clear logical infrastructure: FOL (too expressive for decidability, more intuitive (human friendly) than e.g. OWL) • much more coherent than e.g. CYC upper level • much more coherent than the upper level hard wired into OWL-DL (and a fortiori into OWL-FULL) • FOL

    14. problems with SUMO as Upper-Level • it contains its own tiny biology (protein, crustacean, fruit-Or-vegetable ...) • it is overwhelmingly an ontology for abstract entities (sets, functions in the mathematical sense, ...) • no clear treatment of relations between instances vs. relations between types • [all of these problems can be fixed]

    15. EXPO: Experiment Ontology

    16. representational style part_of experimental hypothesisexperimental actions part_of experimental design

    17. equipment part_of experimental design (confuses object with specification)

    18. OBI • The Ontology of Biomedical Investigations • grew out of FuGE, FuGO, MGED, PSI development activities

    19. Overview of the Ontology of Biomedical Investigations with thanks to Trish Whetzel (FuGO Working Group)

    20. OBI née FuGO Purpose • Provide a resource for the unambiguous description of the components of biomedical investigations such as the design, protocols and instrumentation, material, data and types of analysis and statistical tools applied to the data • NOT designed to model biology Enables • consistent annotation of data across different technological and biological domains • powerful queries • semantically-driven data integration

    21. Motivation for OBI Standardization efforts inbiologicaland technological domains • Standard syntax - Data exchange formats • To provide a mechanism for software interoperability, e.g. FuGE Object Model • Standard semantics - Controlled vocabularies or ontology • Centralize commonalities for annotation term needs across domains to describe an investigation/study/experiment, e.g. FuGO

    22. Emerging FuGO Design Principles OBO Foundry ontology, utilize ontology best practices • Inherit top level classes from an Upper Level ontology • Use of the Relation Ontology • Follow additional OBO Foundry principles • Facilitates interoperability with other OBO Foundry ontologies Open source approach • Protégé/OWL • Weekly conference calls • Shared environment using Sourceforge (SF) and SF mailing lists

    23. OBI Collaborating Communities • Crop sciences Generation Challenge Programme (GCP), • Environmental genomics MGED RSBI Group, • Genomic Standards Consortium (GSC), • HUPO Proteomics Standards Initiative (PSI), • Immunology Database and Analysis Portal, • Immune Epitope Database and Analysis Resource (IEDB), • International Society for Analytical Cytology, • Metabolomics Standards Initiative (MSI), • Neurogenetics, Biomedical Informatics Research Network (BIRN), • Nutrigenomics MGED RSBI Group, • Polymorphism • Toxicogenomics MGED RSBI Group, • Transcriptomics MGED Ontology Group

    24. OBI also includes Clinical Trial Ontology

    25. FuGO - Top Level Universals Continuant: an entity that endure/remains the same through time • Dependent Continuant: depend on another entity E.g. Environment (depend on the set of ranges of conditions, e.g. geographic location) E.g. Characteristics (entity that can be measured, e.g. temperature, unit) - Realizable: an entity that is realizable through a process (executed/run) E.g. Software (a set of machine instructions) E.g. Design (the plan that can be realized in a process) E.g. Role (the part played by an entity within the context of a process) • Independent Continuant: stands on its own E.g. All physical entity (instrument, technology platform, document etc.) E.g. Biological material (organism, population etc.) Occurrent: an entity that occurs/unfold in time • E.g. Temporal Regions, Spatio-Temporal Regions (single actions or Event) • Process E.g. Investigation (the entire ‘experimental’ process) E.g. Study (process of acquiring and treating the biological material) E.g. Assay (process of performing some tests and recording the results)

    26. Basic Formal Ontology • a true upper level ontology • no interference with domain ontologies • no interference with physics / cognition • no abstracta • no negative entities • explicit treatment of instances, types and relations

    27. Three dichotomies • instance vs. type • continuant vs. occurrent • dependent vs. independent • everything in the ontology is a type • types exist in reality through their instances

    28. instance vs. type • experiments as instances • experiments as types • ontologies relate to types (kinds, universals) • we need to keep track of instances in databases

    29. BFO Continuant Occurrent (Process) Independent Continuant Dependent Continuant ..... ..... ........

    30. BFO Continuant Occurrent (Process) Independent Continuant (molecule, cell, organ, organism) Dependent Continuant (quality, function, disease) Functioning Side-Effect, Stochastic Process, ... ..... ..... .... .....

    31. Image Ontology Document Ontology Phenotype (Quality) Ontology Unit Ontology

    32. Measurements and the Unit Ontology with thanks to Chris Mungall

    33. Reality has various measurable dimensions • length • weight • temperature • specific gravity • etc.

    34. Fiat boundaries • The product of (our) gridding activity

    35. Artist’s Grid

    36. ... 0 10 10  20 20  30 30  40 ... massively increased ... normal increased chronic... Measurement belongs to the realm of partitions

    37. An Act of Measurement portion of reality: dependent magnitude (here: distance) + independent bearer

    38. The Act of Measurement tape measure (grid) projected onto reality with endpoints mapped to endpoints l l l l l l l l l l l l l l l l l l l l l l l

    39. Scalar qualities • A scalar quality can be partitioned on a linear scale • fiat boundaries • Scalar qualities can be measured • Measurements involve units • A unit is a fiat subtype of a scalar qualities • Measurements are the simplest sorts of experiments (depend on equipment ...)