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BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
BioPortal: ontologies and integrated data resourcesat the click of a mouse
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BioPortal: ontologies and integrated data resources at the click of a mouse

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Invited presentation at the French Medical Semantic Web workshop 2010 in Nimes. Presentation done in several seminars since then.

Invited presentation at the French Medical Semantic Web workshop 2010 in Nimes. Presentation done in several seminars since then.

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  • Let’s try to understand the context of this work and what we mean by semantic annotation.
  • Common infrastructure for Notes using the Changes and Annotation Ontology (ChAO)
  • Users create notes in order todiscuss class definitionssuggest changes and correctionsrequest new itemsprovide additional information about a class (e.g., references, supporting documentation)
  • found by the tools (efficient, but far from perfect)specified by users (low throughput, but better quality)
  • Les découvertes qui pourraient être réalisées par la fouille des données biomédicales sont limitées car la plupart des ressources publiques ne sont généralement pas décrites à l’aide de terminologies et d'ontologies Pourquoi est-ce que c’est difficile ?Traiter des données textuelle (TAL, désambiguation, polysémie)Mettre en valeur la connaissance des ontologiesAlgorithmes de graphe (e.g., fermeture transitive is_a sur des ontologies de 300K concepts), Distance sémantique, Alignement entre ontologiesEchelle, Ontologies (différents formats, dispatchées, recoupées)Resource de données énormes, e.g., PubMed 17M citation Ontologies et ressources évoluent au cours du temps: Nouvelle version de GO toutes les nuitsReference: beaucoup de travail fait au niveau de l’annotation de produit de genes… ou de la reconnaissance de nom de proteine ou de gene ou de molecules… mais c’est pas forcement des approches basees sur les ontologies (bien que GO soit le meilleur example de success)Faire ce genre de chose avec les maladies par exemple, reste un vrai challenge. Et les maladies elles sont beaucoup decrites dans des ontologies.
  • Elsevier SciVerseKaren Dowell, Jackson LabShai-shen Orr, Mark Davis’s labSean Mooney’s groupIda Sim, UCSFSimon Twigger, Medical college of WisconsinNathan Baker, Washington Univ.Amit Seth, Wright State Univ.Neil Sarkar, University of VermontLarry Hunter, University of Colorado, Denver
  • Let’s try to understand the context of this work and what we mean by semantic annotation.
  • Ontology based annotation is not wide-spread; possibly because of:Lack of a one stop shop for bio-ontologiesLack of tools to annotate datasetsManual  will not scaleAutomatic  can it be ‘good enough’?Lack of a sustainable mechanism to create ontology based annotations
  • Transcript

    • 1. BioPortal ontologies et ressources de données biomédicales à portée de main…
      Clement Jonquet& BioPortal team
      jonquet@stanford.edu
      Atelier Web Sémantique Médical, Nîmes, France - 8 Juin 2010
      1
    • 2. Présentation de la présentation
      Merci pour cette opportunité
      Contribution de tout le groupe NCBO (~20 pers.)
      Plan
      Présentation générale
      Ce qu’on peut faire avec BioPortal (démo?)
      Discussion
      Article de référence
      N. F. Noy, N. H. Shah, P. L. Whetzel, B. Dai, M. Dorf, N. B. Griffith, C. Jonquet, D. L. Rubin, M. Storey, C. G. Chute, M. A. Musen. BioPortal: ontologies and integrated data resourcesat the click of a mouse. NucleicAcidsResearch, 37:170–173, May 2009.
      2
    • 3. Biologist have adopted ontologies
      To provide canonical representation of scientific knowledge
      To annotate experimental data to enable interpretation, comparison, and discovery across databases
      To facilitate knowledge-based applications for
      Decision support
      Natural language-processing
      Data integration
      But ontologies are: spread out, in different formats, of different size, with different structures
      3
    • 4. What is BioPortal?
      Web repository for biomedical ontologies – “ one stop shop”
      Make ontologies accessible and usable – abstraction on format, locations, structure, etc.
      Users can publish, download, browse, search, comment, align ontologies and use them for annotations both online and via a web services API.
      Community-based ontology development, alignment, and evaluation
      Figures:
      200+ ontologies (OWL, OBO, UMLS)
      ~ 1.7 million terms
      ~ 2 million mappings
      22 annotated biomedical resources
      ~ 10 milliards annotations
      4
    • 5. What are we trying to do
      You’ve built an ontology, how do you let the world know?
      You need an ontology, where do you go o get it?
      How do you know whether an ontology is any good?
      How do you find resources that are relevant to the domain of the ontology (or to specific terms)?
      How could you leverage your ontology to enable new science?
      5
    • 6. Community-based ontology repository
      http://bioportal.bioontology.org
      6
    • 7. BioPortal features
      Library of ontologies (support browsing, visualizing, versioning, metrics, views)
      Search ontologies, resources
      Peer review: comments and discussion
      Mapping
      Annotate data
      7
    • 8. Library of biomedical ontologies
      8
    • 9. Ontology metadata
      9
    • 10. Ontology metrics
      10
      Statistics
      Conformance to
      Best practices
    • 11. Ontology views
      11
      Specific subset
      Other languages
    • 12. Ontology search
      12
      Keywords & options
      Ontologies to use
    • 13. Ontology browsing
      13
    • 14. Ontology visualizing
      14
    • 15. Ontology notes
      15
    • 16. Ontology mappings
      16
    • 17. Mappings in BioPortal
      Ontologies, vocabularies, and terminologies will inevitably overlap in coverage
      Concept-to-concept mappings
      e.g., nostril in NCI Thesaurus is similar to naris in Mouse Anatomy Ontology
      • Found by tools and uploaded in bulk
      • 18. Created by users
      • 19. Provenance
      17
    • 20. How mappings are useful?
      Navigation mechanism, linking one ontology to another
      Annotating & query expansion in search
      Allows to include synonyms defined in other ontologies
      Use for finding “important” or “reference” ontologies
      If everyone maps to NCI Thesaurus, it must be important
      Accessible through web services & RDF to be used in other applications
      18
    • 21. Ontology-based annotation workflow
      19
      First, direct annotations are created by recognizing concepts in raw text,
      Second, annotations are semantically expanded using knowledge of the ontologies,
      Third, all annotations are scored according to the context in which they have been created.
    • 22. Explosion of biomedical data: diverse, distributed, unstructured… not link to ontologies
      • Hard for biomedical researchers to find the data they need
      • 23. Data integration problem
      • 24. Translational discoveries are prevented
      • 25. Good examples
      • 26. GO annotations
      • 27. PubMed (biomedical literature) indexed with Mesh headings
      Annotate data with ontology concepts
      Horizontal approach
      Annotation challenge
      20
      RESOURCES
      ONTOLOGIES
    • 28. NCBO Annotator in BioPortal
      21
    • 29. Code
      Word & Firefox add-ins to call the Annotator Service?
      Excel
      UIMA platform
      Specific UI
      NCBO Annotator service
      Multiple ways to access
    • 30. NCBO Biomedical Resources index
      • We have used the workflow to index several important biomedical resources with ontology concepts (22+)
      • 31. The index can be used to enhance search & data integration
      23
      [DILS 08]
      [BMC BioInfo09]
      [IC 10]
    • 32. Ex: annotation of a GEO element
      24
    • 33. Ontology-based search (1/2)
      Example of resource available (name and description)
      Number of annotations in the NCBO Resource Index
      Ontology concept/term browsed
      Title and URL link to the original element
      Context in which an element has been annotated
      ID of an element
      25
    • 34. Ontology-based search (2/2)
      26
      Ontology concept(s) to use for search
      Keyword to search
      Biomedical resources to query
      Resource elements found
    • 35. Good use of the semantics (1/2)
      • Simple keywords based search miss results
      27
    • 36. 28
      Good use of the semantics (2/2)
    • 37. Ontology recommendation
      29
    • 38. The BioPortal technology
      All BioPortal data is accessible through REST services
      BioPortal user interface accesses the repository through REST services as well
      For example:
      http://bioportal.bioontology.org/visualize/40401/?conceptid=D008545
      http://rest.bioontology.org/bioportal/concepts/40401/?conceptid=D008545
      The BioPortal technology is domain-independent
      BioPortal code is open-source
      Technology stack includes: Protégé, LexGrid, MySQL, Hibernate, Spring, J2EE, Ruby-on-Rails
      30
    • 39. Other installations of BioPortal
      31
    • 40. BioPortal’s future
      Better support of Semantic Web standards
      Done: provide URI for every concept in the ontology
      TBD: ontologies & annotations available through a SPARQL endpoint
      Development of a biomedical mega-thesaurus based on ontology mappings
      Merge ontology editing & publishing
      Scalability
      Distributed architecture
      Enhance views/modularization e.g., different languages
      32
    • 41. Conclusion
      BioPortal is allowing NCBO to experiment with new models for
      Dissemination of knowledge on the Web
      Integration and alignment of online content
      Knowledge visualization and cognitive support
      Peer review of online content
      Exciting context of research & application for both CS and Biomedical informatics
      BioPortal is a good illustration of biomedical semantic web application
      Please try it and join us!
      33
    • 42. Collaborateurs & remerciements
      • @ NCBO, Stanford University
      • 43. Natasha Noy, Mark Musen, Nigam Shah, Patricia Whetzel, Adrien Coulet, Paea Le Pendu, Michael Dorf, Cherie Youn, Paul Alexander, Sean Falconer
      • 44. @ NCBO, somewhere else
      • 45. Peggy Storey, Chris Callendar, Christopher Chute, Pradip Kanjamala, JyotiPathak, Jim Buntrock
      • 46. and many others
      34
    • 47. MerciNational Center for BioMedical Ontologyhttp://www.bioontology.orgBioPortal, biomedical ontology repositoryhttp://bioportal.bioontology.orgContact mejonquet@stanford.edu
      35
    • 48. Develop a mega-thesaurus
      Group mapped concept s from different ontologies to create a single concept
      Similar to the approach taken by NLM with UMLS Metathesaurus
      manual vs. automatic
      36
    • 49. Integration of ontology editing and publishing
      Enable users to go seamlessly between ontology editing and publishing
      Notes created in BioPortal are visible in an ontology editor
      User accounts and roles shared among BioPortal and ontology editors
      Users don’t need to be aware of the difference: they just get their work done
      37
    • 50. Annotation & semantic web
      • Part of the vision for the semantic web
      • 51. Web content must be semantically described using ontologies
      • 52. Semantic annotations help to structure the web
      • 53. Annotation is not an easy task
      • 54. Automatic vs. manual
      • 55. Lack of annotation tools (convenient, simple to use and easily integrated into automatic processes)
      • 56. Today’s web content (& public data available through the web) mainly composed of unstructured text
      38
    • 57. Annotation is not a common practice
      • High number of ontologies
      • 58. Getting access to all is hard: formats, locations, APIs
      • 59. Lack of tools that easily access all ontologies (domain)
      • 60. Users do not always know the structure of an ontology’s content or how to use it in order to do the annotations themselves
      • 61. Lack of tools to do the annotations automatically
      • 62. Boring additional task without immediate reward for the user
      39
    • 63. The challenge
      • Automatically process a piece of raw text to annotate it with relevant ontologies
      • 64. Large scale – to scale up for many resources and ontologies
      • 65. Automatic – to keep precision and accuracy
      • 66. Easy to use and to access – to prevent the biomedical community from getting lost
      • 67. Customizable – to fit very specific needs
      • 68. Smart – to leverage the knowledge contained in ontologies
      40

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