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Collaborative tagging for GO

Collaborative tagging for GO. Domenico Gendarmi Department of Informatics University of Bari. Outline. Semantic Web Web 2.0 Collaborative Tagging An hybrid approach Current case study: digital libraries Potential case study: GO. The Semantic Web three-layers architecture.

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Collaborative tagging for GO

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  1. Collaborative tagging for GO Domenico Gendarmi Department of Informatics University of Bari

  2. Outline • Semantic Web • Web 2.0 • Collaborative Tagging • An hybrid approach • Current case study: digital libraries • Potential case study: GO

  3. The Semantic Web three-layers architecture Sharing a common understanding is a key reason for using ontologies Creating and maintaining knowledge is a human-intensive activity Community Layer Semantic Layer Content Layer

  4. Ontology issues for large-scale knowledge-sharing • Lack of consensus • Formal representations of a specific domain imposed by an authority rather than based on shared understanding among users • Low dynamicity • Knowledge drift asks for reactive changes to ontologies • High entry barriers • Ontology maintenance requires technical skills in knowledge engineering

  5. Web 2.0 principles • Openess • User generated metadata • Interaction • Rich and interactive user interfaces • Community/Collaboration • Social networks • The Web as “the global platform” • Sharing of services & data

  6. Collaborative Tagging systems Tags as user-generated metadata Also known as folksonomies = folk + taxonomies The creation of metadata is shifted from an individual professional activity to a collective endeavor User Resource Tag

  7. What’s new? Collaboration • You can tag items owned by others • Instant feedback • All items with the same tag • All tags for the same item • Communication through shared metadata • Tight feedback loop • Negotiation about the meaning of the terms • You could adapt your tags to the group norm • Never forced

  8. U1 R1 T1 U2 R2 T2 R3 U3 T3 A formal model of collaborative tagging systems • Tripartite 3-uniform hypergraph • N U  T  R E {(u,t,r) | uU, tT, rR)} F (N,E) <triple> <qname>u:U1</qname> <qname>t:T2</qname> <qname>r:R3</qname> </triple> T1 U1 R1 <triple> <qname>u:U2</qname> <qname>t:T3</qname> <qname>r:R2</qname> </triple> T2 U2 R2 <triple> <qname>u:U3</qname> <qname>t:T1</qname> <qname>r:R1</qname> </triple> R3 U3 T3

  9. Collaborative Tagging applications • Social Bookmarking • Del.icio.us, Fuzzzy, Simpy • Social Media sharing • Flickr, YouTube, Last.fm • Social reference management • CiteULike, Bibsonomy, Connotea • Other… • Anobii, Library Thing, 43 things, …

  10. del.icio.us: popular bookmarks and tags

  11. del.icio.us: bookmark details

  12. del.icio.us: saving a bookmark

  13. Benefits Reflects user vocabulary Sensitive to knowledge drift Creates a strong sense of community Emerging consensus Limits Synonymy Polysemy Basic level variation Low precision & recall Collaborative tagging trade-off

  14. Our vision • A community of users which collaborate for collectively evolving an initial knowledge structure (lightweight ontology) • Help users in the organization of personal information spaces • Bring together different contributions to reflect the community common ground

  15. Proposed approach: 3-step iteration • Users select information they are interested into • Users organize their personal information spaces • Individual contributions are grouped to create shared information spaces

  16. Step 1: Selection

  17. Personal Information Space B1 Bn cx c1 c3 cy c4 cz … … Topic a … … Topic k Personal Taxonomy User Profile Step 2: Organization • Choose binder name • Browse space of metadata • Select metadata • Update personal taxonomy

  18. Step 3: Sharing • Share personal binders • Browse shared information spaces • Express preferences on shared taxonomies

  19. Gene Ontology Context • GO can be used for the annotations of a large amount of gene products • Two relationship types • is-a • part-of • Roles • Curators • Annotators

  20. Three-step iteration applied to GO • Step 1: Selection • Using existing tools for browsing GO (i.e. AmiGO) scientists could select genes/gene products they are interested into • Step 2: Organization • Scientists could create and organize their own private working spacewhere to annotate the selected genes with GO terms (existing or new ones) • Step 3: Sharing • Sharing personal information about gene products among people or groups with similar research interests could evolve the knowledge about selected genes by many individuals

  21. Claims of verify • Personal information spaces could help scientists in laboratories to organize their own knowledge on gene products using their favourite terms, descriptions and annotations • Knowledge sharing among scientists with similar interests could create a feedback loop like in folksonomies • The GO could significantly benefit from this combination of ‘quasi uncontrolled’ knowledge spaces of scientists in the laboratories and a central organized knowledge structure

  22. References • F. Abbattista, F. Calefato, D. Gendarmi and F. Lanubile, Shaping personal information spaces from collaborative tagging systems, KES 2007/ WIRN 2007, Part III, LNAI 4694, pp. 728–735, 2007. • D. Gendarmi, F. Abbattista and F. Lanubile, Fostering knowledge evolution through community-based participation, Proc. of the Workshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007), at the 16th International World Wide Web Conference (WWW 2007). • D. Gendarmi and F. Lanubile, Community-Driven Ontology Evolution Based on Folksonomies, OTM Workshops 2006, LNCS 4277, pp. 181–188, 2006.

  23. Acknowledgments Thank you to: • Prof. Filippo Lanubile • Dr. Andreas Gisel Contact: • Domenico Gendarmi University of Bari, Dipartimento di Informatica Collaborative Development Group http://cdg.di.uniba.it/

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