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Collaborative tagging for GO

This paper discusses collaborative tagging within the context of the Semantic Web, emphasizing its application in digital libraries and Gene Ontology (GO). It presents a hybrid approach that merges Web 2.0 principles with ontology management to improve knowledge organization and sharing. Key issues such as lack of consensus, dynamicity, and high entry barriers are addressed, showcasing the potential for user-generated metadata to foster community engagement. The proposed three-step iteration process aids users in selecting, organizing, and sharing personal information spaces, enhancing collaborative knowledge creation.

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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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