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

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Presentation on theme: "Collaborative tagging for GO Domenico Gendarmi Department of Informatics University of Bari."— Presentation transcript:

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 Tag ResourceUser

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 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) U1 T1 R1 U2 T2 R2 U3T3 R3 U1 T2 R3 U2 R2 T3 T1 R1 U3 u:U1 t:T2 r:R3 u:U2 t:T3 r:R2 u:U3 t:T1 r:R1

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 Collaborative tagging trade-off 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

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 personal information spaces Users organize their personal information spaces shared information spaces Individual contributions are grouped to create shared information spaces

16 Step 1: Selection

17 Step 2: Organization Choose binder name Browse space of metadata Select metadata Update personal taxonomy B1B1 c1c1 c4c4 c3c3 Bn cxcx czcz cycy … Personal Information Space Personal Taxonomy User Profile Topic a … Topic k

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 space where 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, 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


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