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BioPortal: ontologies and integrated data resourcesat the click of a mouse
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
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.
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
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
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
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43. Natasha Noy, Mark Musen, Nigam Shah, Patricia Whetzel, Adrien Coulet, Paea Le Pendu, Michael Dorf, Cherie Youn, Paul Alexander, Sean Falconer
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
68. Smart – to leverage the knowledge contained in ontologies40
Editor's Notes
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