1 The Ontology of Experiments + PATO Barry Smith

2 Plan 1.The Experiment Ontology 2.Upper Level Ontologies 3.The Ontology of Biomedical Investigations 4.Phenotype 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.

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9 Suggested Upper Merged Ontology Adam Pease

10 SUMO top level Entity –PhysicalPhysical Object –SelfConnectedObjectSelfConnectedObject »SubstanceSubstance »CorpuscularObjectCorpuscularObject »FoodFood –RegionRegion –CollectionCollection –AgentAgent Process –AbstractAbstract SetOrClass Relation Quantity –NumberNumber –PhysicalQuantityPhysicalQuantity Attribute Proposition

11 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]

12 SUMO Structure Structural OntologyBase OntologySet/Class TheoryNumericTemporal Mereotopology GraphMeasureProcessesObjectsQualities

13 SUMO+Domain Ontology Structural Ontology Base Ontology Set/Class Theory NumericTemporal Mereotopology GraphMeasureProcessesObjects Qualities SUMO Mid-Level Military Geography Elements Terrorist Attack Types Communications People Transnational Issues Financial Ontology Terrorist EconomyNAICS Terrorist Attacks … France Afghanistan UnitedStates Distributed Computing Biological Viruses WMD ECommerce Services Government Transportation World Airports Total Terms Total Axioms Rules

14 entity physical object process dual object process intentional process intentional psychological process recreation or exercise organizational process guiding keeping maintaining repairing poking content development making constructing manufacture publication cooking searching social interaction maneuver motion internal change shape change abstractentity physical object process dual object process intentional process intentional psychological process recreation or exercise organizational process guiding keepingmaintaining repairing poking content development makingconstructing manufacturepublicationcooking searching social interactionmaneuver motion internal changeshape change abstract

15 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.

16 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

17 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]

18 EXPO: Experiment Ontology

19 representational style part_of experimental hypothesis experimental actions part_of experimental design

20 equipment part_of experimental design (confuses object with specification)

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22 OBI The Ontology of Biomedical Investigations grew out of FuGE, FuGO, MGED, PSI development activities

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

24 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

25 Motivation for OBI Standardization efforts in biological and 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

26 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

27 OBI Collaborating Communities Crop sciences Generation Challenge Programme (GCP), Environmental genomics MGED RSBI Group, Genomic Standards Consortium (GSC), HUPO Proteomics Standards Initiative (PSI), psidev.sourceforge.net 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

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30 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)

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33 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

34 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

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

36 BFO Continuant Occurrent (Process) Independent Continuant Dependent Continuant

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

38 BFO Continuant Occurrent (Process) Independent Continuant (molecule, cell, organ, organism) PATOFunctioning Side-Effect, Stochastic Process,

39 Unifying goal: integration Integrating data –within and across these domains –across levels of granularity –across different perspectives Requires –Rigorous formal definitions in both ontologies and annotation schemas

40 Some thoughts on the ontology itself Outline –Definitions how do we define PATO terms? what exactly is it we’re defining? –is_a hierarchy what are the top-level distinctions? what are the finer grained distinctions? –shapes and colors

41 It’s all about the definitions OBO Foundry Principle –Definitions should describe things in reality, not how terms are used definitions should not use the word ‘describing’ Scope of PATO = Phenotypic qualities

42 Old PATO Entity – Attribute – Value Eye – Red – Dark New PATO Entity – Quality Eye – Red Eye – Dark Red Dark Red is_a Red

43 What a quality is NOT Qualities are not measurements –Instances of qualities exist independently of their measurements –Qualities can have zero or more measurements These are not the names of qualities: –percentage –process –abnormal –high

44 Some examples of qualities The particular redness of the left eye of a single individual fly –An instance of a quality type The color ‘red’ –A quality type Note: the eye does not instantiate ‘red’ PATO represents quality types (universals) –PATO definitions can be used to classify quality instances by the types they instantiate

45 the particular case of redness (of a particular fly eye) the type “red” instantiates an instance of an eye (in a particular fly) the type “eye” instantiates inheres in (is a quality of, has_bearer)

46 Qualities are dependent entities Qualities require bearers –Bearers can be physical objects or processes Example: –A shape requires a physical object to bear it –If the physical object ceases to exist (e.g. it decomposes), then the shape ceases to exist

47 Proposal 1: top level division Spatial quality –Definition: A quality which has a physical object as bearer –Examples: color, shape, temperature, velocity, ploidy, furriness, composition, texture Spatiotemporal quality –Definition: A quality which has a process as bearer –Examples: rate, periodicity, regularity, duration

48 ScaleBearerQualityDefinition (proposed) PhysicalContinuantMass Equivalent to the sum of the mass of the parts of the bearer (mass at the particle level is primitive/outwith PATO) PhysicalContinuantOpacity An optical quality manifest by the capacity of the bearer to block light Phys/ChemLiquidConcentration A compositional relational quality manifest by the relative quantity of some chemical type contained by the bearer MolecularGenesplicing quality manifest by the splicing processes undergone by the bearer CellularCellploidy A cellular quality manifest by the number of genomes that are part of the bearer CellularCelltransformative potency?? A cellular quality manifest by the capacity of the bearer cell to differentiate to different cell types OrganismalTissuetone OrganismalOrganismreproductive quality

49 How many types of shape are there? notched, T-shaped, Y-shaped, branched, unbranched, antrose, retrose, curled, curved, wiggly, squiggly, round, flat, square, oblong, elliptical, ovoid, cuboid, spherical, egg-shaped, rod-shaped, heart-shaped, … How do we define them? How do we compare them? Shapes cannot be organized in a linear scale Compare: problem of classifying RNA structures

50 Standard case: monadic qualities Examples –E=kidney, Q=hypertrophied –autodef: a kidney which is hypertrophied We assume that there is more contextual data (not shown) –e.g. genotype, environment, number of organisms in study that showed phenotype Interpretation (with the rest of the database record): –all fish in this experiment with a particular genotype had a hypertrophied kidney at some point in time

51 Who should use PATO? Originally: – model organism mutant phenotypes But also: –ontology-based evolutionary systematics –neuroscience; BIRN –clinical uses OMIM clinical records (clinical manifestations) drug effects, chemical effects –to define terms in other ontologies e.g. diploid cell; invasive tumor, engineered gene, condensed chromosome