FaceTag - IASummit 2007

FaceTag Integrating Bottom-up and Top-down Classification in a Social Tagging System IA Summit 2007 22-26 March, Las Vegas

The Long Tail Our culture and economy is increasingly shifting away from a focus on a relatively small number of "hits" at the head of the demand curve and toward a huge number of niches in the tail

Costs of production fall

Costs of storage and distribution fall

We need better filters and aggregators

The UGC Era

Among adults, 35% of internet users have created content and posted it online

User generated content can be anything produced by the user: text, audio, video, photos, categories or ranks, clips

(PEW, December 2005)

Information Overload

601 exabytes in 2010

70 percent of the world's digital data will be created by individuals

(IDC 2007)

185 exabytes (billion gigabytes) of storage needed last year

12 stacks of books that each reach from the Earth to the Sun

3 million times the information in all the books ever written

more than 2 billion of the most capacious iPods

How can we navigate and find this information?

An Emerging Approach to Distributed Classification

Collaborative tagging systems to organize, browse and share personal collections of resources through the introduction of simple metadata

Folksonomy = user-generated classification, emerging through bottom-up consensus

The basic idea is simply to make people share items annotated with keywords

Del.icio.us Homepage

Collaborative Tagging Examples

An incomplete list of tagging systems

They are web-based collaborative systems for:

building a shared database of items

a flat metadata vocabulary

metadata driven queries

to monitor change in areas of interest

discover emergencies or trends

Have tagging systems solved the issue?

Del.icio.us Homepage

Del.icio.us Tagcloud

Food Tag in Del.icio.us

Down the Long Tail TAG CLOUDS HITS LONG TAIL ?

More Tags for the Same Thing

How many apples? VS VS VS

Folksonomies ’ Properties

Pros

Trade-off between simplicity and precision

Matching users’ real needs and language

They are inclusive (nothing is left out)

Discovery of information and Serendipity

A forced move (the environment makes the difference)

Better than nothing (when traditional classification is not viable)

Cons

Language related issues

User Experience issues

Language Issues

As a result of intrinsic language variability, tagging systems are also implicitly plagued by:

Polysemy (window: the hole or the pane of glass)

Homonymy (apple, jaguar)

Plurals (blog/blogs, folksonomy/folksonomies)

Mistyped tags (folsonomy)

Synonymy (tags, tagging, folksonomy)

Ego-oriented tags (toread, funny, interesting etc..)

Basic level variations (dog/beagle)

These problems can dramatically reduce the effectiveness of the application and the benefits brought on by tagging systems.

User Experience Issues

Low findability quotient and Low scalability

High semantic density (very few well-known topics dominating the scene)

An alphabetical criterion limits the ability to explore the tag cloud

A flat tag cloud cannot visually support semantic relationships

Tag clouds are visual interfaces for information retrieval that provide a global contextual view of tags assigned to resources in the system

Flat tag clouds not sufficient to provide a semantic and multidimensional browsing experience

Navigating Large Domains

Information seekers in large domains of objects express the desire of having to deal with meaningful groupings of related items, in order to quickly understand relationships and decide how to proceed [Marti Hearst 2006].

How to generate and navigate such groups from a flat set of objects is anyway a different matter.

Taxonomies, Clustering and Faceted Classification have been proposed in the past as useful techniques

Our Toolbox!

Taxonomies

Taxonomies = coherent and complete system of meaningful labels which systematically organize a domain

Organically crafted before starting to catalogue trying to guess user needs and content types

Authoritative centralized view

High precision avoiding ambiguity, hierarchical structure to give context

Main drawbacks:

An a priori and monolithic hierarchical organization do not have the ability to match the vocabulary and the varied ways of thinking of different users.

Expensive to build and maintain by professional indexers

Clusters

Clustering = the act of grouping items according to some measure of similarity

It reduces the semantic density and improve the visual consistency of tag clouds

But it generate messy groups , conflate many different dimensions and does not allow refinement and follow-up queries

Users prefer clear hierarchies with categories at uniform levels of granularity over the messy, unpredictable and unlabeled groupings typical of clustering techniques

Facets

Facets = orthogonal descriptors (categories) within a metadata system

Each facet has a name and addresses a different conceptual dimension or feature type relevant to the collection

Each object is classified combining labels from different facets.

Facets can be :

Flat or hierarchical

Assigned single or multiple values

It is hierarchial Facet name 393 resources here

Facets

add structure and context to tags

navigate along several dimensions simultaneously

seamlessly integrate browsing and searching

refine and broaden filtering criteria

Benefits

Hierarchical faceted metadata can be used to

Better support for exploration , discovery and iterative query refinement.

Easier to understand the meaning of tags

Large tag clouds more browsable

Reduction of the mental work (favoring recognition over recall)

Usability studies show how this approach is preferred over single hierarchies and clusters

What do we need next?

What do we need yet?

Middle ground (between the pure democracy of bottom-up tagging and the empirical determinism of top-down controlled vocabularies*)

Metadata ecology : merge and leverage emerging and traditional tools to improve findability

Metadata ecology as a fusion not only a coexistence

*Alex Wright: http://www.agwright.com/ blog / archives /000900.html

FaceTag Contributions

FaceTag tries to limit the impact of polysemy, homonymy and basic level variation introducing a multidimensional and more semantic paradigm

Goals : improving usability, findability, browsability, serendipity and scalability of the system.

FaceTag mixes three contributions to social tagging systems:

Tag hierarchies

Facets of Tags

Tagging and searching mixed

Faceted Analysis

How to choice facets? 2 main roads

Freehand made

Each facet is created at the moment

The subject is freely deconstructed in several aspects

Standard based

Each facet is chosen using the guidelines fixed by Ranganathan or CRG

The subject is deconstructed following a “general scheme”

“ General scheme” works as a prototype of every particular faceted scheme

Facets derived from the CRG general scheme Date Time [Country] Space People Agent Purposes, [Markets] (e.g. Industry, Health ...) Patient -- By-product Deliverables Product [Activities] Operation -- Process Themes Material Language Property -- Part Resource Types (e.g. online report, case study, blog...) Type [Documents, Resources] Thing FACETAG CRG

FaceTag facets date of publication (through a calendar) Date public administration health, education > conferences > www2006 Purposes Weinberger e. Reiss Morville People competitive analysis classification > facets web 2.0 > folksonomies information design > navigation design > breadcrumbs Themes predefined values (select box ISO Standard ISO 639-2) Language white paper case study blog > enterprise web Resource Types SAMPLES FACETAG FACET

The Real Application

FaceTag Home SEARCH + TAG SUGGESTION FACETS + FACETED TAGS RESULT SET

Search Term Selected SEARCH TERM UPDATED TAGS UPDATED RESULT SET

Search Term + Tag Selection FACET TAGS for this FACET FACETED BREADCRUMBS UPDATED RESULT SET

Final Step UPDATED TAGS RESULT SET

Input Form WYSIWYG EDITOR FACETS TITLE and URL HIERARCHICAL TAG SUGGESTION

Preliminary User Research