Collaborative tagging for GO

Domenico GendarmiDepartment 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 Sharing a common

understanding is a key reason for using ontologies

Creating and maintaining knowledge is a human-intensive activity

Community LayerCommunity Layer

Semantic LayerSemantic Layer

Content LayerContent Layer

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

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

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

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

A formal model of collaborative tagging systems Tripartite 3-uniform hypergraph

N U T R E {(u,t,r) | uU, tT, rR)}

F (N,E)

U1 T1 R1

U2 T2 R2

U3 T3 R3

U1

T2

R3

U2 R2

T3

T1 R1

U3

<?xml-stylesheet type="text/xml" href="http://www.w3.org/2004/03/trix/all.xsl"?> <TriX xmlns="http://www.w3.org/2004/03/trix/trix1/" xmlns:u="http://example.com/userentity/"> xmlns:t="http://example.com/tagentity/"> xmlns:r="http://example.com/resourceentity/"> <graph> <uri>http://example.org/folksonomy</uri> <triple> <qname>u:U1</qname> <qname>t:T2</qname> <qname>r:R3</qname> </triple> <triple> <qname>u:U2</qname> <qname>t:T3</qname> <qname>r:R2</qname> </triple> <triple> <qname>u:U3</qname> <qname>t:T1</qname> <qname>r:R1</qname> </triple> </graph>

<triple> <qname>u:U1</qname> <qname>t:T2</qname> <qname>r:R3</qname> </triple> <triple> <qname>u:U2</qname> <qname>t:T3</qname> <qname>r:R2</qname> </triple>

<triple> <qname>u:U3</qname> <qname>t:T1</qname> <qname>r:R1</qname> </triple>

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, …

del.icio.us: popular bookmarks and tags

del.icio.us: bookmark details

del.icio.us: saving a bookmark

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

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

Proposed approach: 3-step iteration Users select

information they are interested into

Users organize their personal personal information spacesinformation spaces

Individual contributions are grouped to create shared information shared information spacesspaces

Step 1: Selection

Step 2: Organization Choose binder

name Browse space of

metadata Select metadata Update personal

taxonomy

B1

c1

c4

c3

Bncx

cz

cy… …

Personal Information Space

Personal Taxonomy

User Profile

Topic a

…

…

Topic k

Step 3: Sharing

Share personal binders

Browse shared information spaces

Express preferences on shared taxonomies

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

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

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

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.

Acknowledgments

Thank you to: Prof. Filippo Lanubile Dr. Andreas Gisel

Contact: Domenico Gendarmi

University of Bari, Dipartimento di InformaticaCollaborative Development Group http://cdg.di.uniba.it/