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Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
Ontological realism as a strategy for integrating ontologies
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Ontological realism as a strategy for integrating ontologies

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Presented at Ontolog Forum as part of 2013 Ontology Summit

Presented at Ontolog Forum as part of 2013 Ontology Summit

Published in: Education, Spiritual
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    • 1. Ontological realism as a strategy forintegrating ontologiesOntology Summit February 7, 2013Barry Smithhttp://ontology.buffalo.edu/smith1
    • 2. • Scientific data is stored in databases• There are very few constraints on thecreation of new databases• Scientific data becomes ever more siloed• Proposal to counteract this silo-formation:create a common non-redundant suite ofontologies covering all scientific domainsto annotate scientific data• Question: What strategy shall we use tobuild this suite of ontologies2
    • 3. More precisely:How shall we build a common ontology =an ontology that will integrate well withontologies built for neighboring domains?This question arises in science, but it arisesalso in other domains, such as commerce,government and the military3
    • 4. The Semantic Web provides onlya small part of the solution• html demonstrated the power of the Web toallow sharing of information• can we use semantic technology to create aWeb 2.0 which would allow algorithmicreasoning with online information based onXLM, RDF and above all OWL (Web OntologyLanguage)?• can we use RDF and OWL to break down silos,and create useful integration of on-line data andinformation4/24
    • 5. Ontology success stories, andsome reasons for failure•A fragment of the “Linked OpenData” in the biomedical domain5Not all of the links hereare what they seem
    • 6. The more Semantic Technology issuccessful, they more it fails tosolve the problem of silosIndeed it leads to the creation of multiple,new, semantic silos – because multipleontologies are being created in ad hoc ways6/24
    • 7. 7
    • 8. 8
    • 9. 9
    • 10. There are many ways to create ontologies10How do we build ontologies so that they converge?(The problem of „knowledge representation‟)
    • 11. New York StateCenter of Excellence inBioinformatics & Life SciencesR T U New York StateCenter of Excellence inBioinformatics & Life SciencesR T UWhat is going on here ?ah sh uqwu ja ?ywye hasuw has!
    • 12. There are many ways to create ontologies12
    • 13. Evidence-based ontologydevelopmentQ: What is to serve as constraint?A1: Authority (I tell you what to do)A2: Homesteading (Founder effect)A3: Best candidate terminologyBut what does ‘best’ mean?A4: Voting ?But then on what grounds should people vote?13
    • 14. A5: Reality, as revealed,incrementally, by which results ofexperimentation becomeincorporated into textbook science14
    • 15. Three Levels to Keep Straight• Level 1: the entities in reality, bothinstances and universals• Level 2: cognitive representations of thisreality, e.g. on the part of scientists ...• Level 3: publicly accessible concretizationsof these cognitive representations in textualand graphical artifacts15
    • 16. New York StateCenter of Excellence inBioinformatics & Life SciencesR T U New York StateCenter of Excellence inBioinformatics & Life SciencesR T UL1L2L3What is going on here ?ah sh uqwu ja ?ywye hasuw has!
    • 17. The realist approachprovides the basis for coordination(consistency) at the level of specific domains– reflecting the consistency of textbookscienceBut integration requires more than consistency– it requires also a common realistmethodology for ontology developmentSmith and Ceusters, “Ontological Realism as aMethodology for Coordinated Evolution of ScientificOntologies”, Applied Ontology, 5 (2010), 139–188.17
    • 18. Benefits of common methodologyNo need to reinvent the wheel for each newdomainCan profit from storehouse of lessons learnedCan more easily reuse what is made by othersCan more easily reuse trainingCan more easily inspect and criticize results ofothers‟ workLeads to innovations (e.g. Mireot, Ontofox) instrategies for combining ontologies18
    • 19. Candidate Upper Level Ontologies– Domain Ontology for Linguistic and CognitiveEngineering (DOLCE)– Suggested Upper Merged Ontology (SUMO)– Upper Cyc Ontology– Basic Formal Ontology– all reflections of recognized need for semanticstandardization via upper level ontology19
    • 20. Why choose BFOVery smallDesigned to support the consistent representationof different domains of reality in support ofscientific research– integration of data via ontologies presupposesconsistent ontologiesAssociated with aggressive program of project-based testing, feedback and training20
    • 21. Main reason to use BFOBFO has the largest body of scientist users(compare: This telephone network has thelargest number of subscribers)Snowballing network effects:data annotated using BFO-conformantontologies becomes more valuablenumbers of people with expertise inbuilding BFO-conformant ontologiesincreases21
    • 22. Basic Formal OntologyNo fictionsNo non-existentsNo „possible worlds‟BFO is designed to support scientific research• Science is distinct from story-telling• There is no science of unicorns (though they maybe a branch of psychiatry which deals withunicorn-obsessions or unicorn-delusions)22
    • 23. Basic Formal Ontology• a true upper level ontology• no interference with domain ontologies• no interference with issues of cognition a small subset of DOLCE but with aclearer treatment of instances,universals, relations and qualities, time23
    • 24. RELATIONTO TIMEGRANULARITYCONTINUANT OCCURRENTINDEPENDENT DEPENDENTORGAN ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)OrganFunction(FMP, CPRO) PhenotypicQuality(PaTO)BiologicalProcess(GO)CELL ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA, GO)CellularFunction(GO)MOLECULEMolecule(ChEBI, SO,RnaO, PrO)Molecular Function(GO)Molecular Process(GO)The Open Biomedical Ontologies (OBO) Foundry24
    • 25. CONTINUANT OCCURRENTINDEPENDENT DEPENDENTORGAN ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)OrganFunction(FMP, CPRO) PhenotypicQuality(PaTO)Organism-LevelProcess(GO)CELL ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA, GO)CellularFunction(GO)Cellular Process(GO)MOLECULEMolecule(ChEBI, SO,RNAO, PRO)Molecular Function(GO)MolecularProcess(GO)rationale of OBO Foundry coverage(homesteading principle)GRANULARITYRELATION TOTIME25
    • 26. Users of BFO (Consortia)26NIF Standard NeuroscienceInformation Frameworkeagle-I Ontologies used by VIVO andCTSAconnectIDO Consortium Infectious DiseaseOntologyCROP Common ReferenceOntologies for Plants
    • 27. – GO Gene Ontology– CL Cell Ontology– ChEBI Chemical Ontology– PRO Protein Ontology– OBI Ontology for Biomedical Investigations– OGMS Ontology for General Medical Science– PATO Phenotype (Quality) Ontology– IDO Infectious Disease Ontology– PO Plant Ontology– SO Sequence Ontology– FMA Foundational Model of Anatomy– CARO Common Anatomy Reference Ontology– EnvO Environment Ontology– Disease OntologyThe OBO Foundry27
    • 28. http://www.ifomis.org/bfo/usersNeurological Disease Ontologies (ND)Interdisciplinary Prostate Ontology (IPO)Nanoparticle Ontology (NPO): Ontology for CancerNanotechnology ResearchNeural Electromagnetic Ontologies (NEMO)ChemAxiom – Ontology for ChemistryOntology for Risks Against Patient Safety (RAPS/REMINE)(EU FP7)Petrochemicla OntologyUS Army (I2WD)…28
    • 29. Example: The Cell Ontology

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