Enhancing social tagging with a knowledge

organization system

Brian MatthewsSTFC

Outline

Who are STFC ? Controlled Vocabulary Social Tagging EnTag

– Aims– Glamorgan/UKOLN/Intute Experiment– STFC Experiment

SKOS

Science and Technology Facilities Council

Provide large-scale scientific facilities for UK Science – particularly in physics and astronomy

E-Science Centre – at RAL and DL– Provides advanced IT development and services

to the STFC Science Programme

– Also includes library and institutional repository

– Strong interest in Digital Curation of our science data

– Keep the results alive and available– R&D Programme:

• DCC, CASPAR• EnTag

Controlled Vocabulary

Traditional way of providing subject classification

– For shelf-marking– For searching– For association of resources

Several different types used, such as – Subject Classification– Keyword lists– Thesaurus

Each has different characteristics

HASSET (I) UK Data Archive, Univ of Essex

Humanities and Social Science Electronic Thesaurus

Some 1000’s of terms

Structure based on British Standard 5723:1987/ISO 2788-1986 (Establishment and development of monolingual thesauri).

preferred terms, broader-narrower relations, associated terms

http://www.data-archive.ac.uk/search/hassetSearch.asp

HASSET (II)

HASSET (III)

Observations on using controlled vocabularies

Precise classification of resources – Good for precision and recall

Can exploit the hierarchy to modify query– Using the broader/narrower/related terms

Highly expensive – Requires investment in specialist

expertise to devise the vocabulary– Requires investment in specialist

expertise to classify resources. Hard to maintain currency

Social Tagging

The Web 2.0 way of providing search terms People “tag” resources with free-text terms of their own choosing Tags used to associate resources together del.icio.us, flickr

“Folksonomy”– the terms a community choses to use to

tag its resources.

Connotea

Connotea – sharing tags

Connotea –Tag Cloud

Observations on Social Tagging

People often use the same tags or keywords (e.g. Preservation, Digital Library)

– this makes things which mean the same thing to people easier to find Cheap way of getting a very large number of resources marked up and classified

– Represents the “community consensus” in some sense– “The Wisdom Of Crowds”– Has currency as people update– Tag clouds of popular tags

However, people often use similar but not the same tags:– e.g. Semantic Web, SemanticWeb, SemWeb, SWeb

People make mistakes in tags– mispellings, using spaces incorrectly.

Some tags are more specific than others:– E.g. controlled vocabulary, thesaurus, HASSET

People often associate the same words together with particular ideas in images

– these are captured in clusters

EnTag Project

Enhanced tagging for discovery

JISC funded project Partners

– UKOLN– University of Glamorgan– STFC– Intute– Non-funded

• OCLC Office of Research, USA• Danish Royal School of Library and Information Science

Period: 1 Sep 2007 -- 30 Sep 2008

http://www.ukoln.ac.uk/projects/enhanced-tagging/

EnTag Background

Controlled vocabularies– Improve information retrieval and discovery– But, costly to index with, especially the amount of

digital documents– Require subject and classification experts

Social tagging – Holds the promise of reducing indexing costs– Uses terms describing how people see the resource– Serendipity– But, tags uncontrolled,

• missed associations• Relating different views• Highly personal (“me”, “important”), • Quality and ranking • Depth of term

•

EnTag Purpose

Investigate the combination of controlled and social tagging approaches to support resource discovery in repositories and digital collections

Aim to investigate – whether use of an established controlled

vocabulary can help move social tagging beyond personal bookmarking to aid resource discovery

EnTag ObjectivesInvestigate indexing aspects when using only social tagging versus when using social tagging in combination with a controlled vocabulary

In particular, does this lead to:

Improve tagging– Relevance of tags (perspective, aspects, specificity,

exhaustivity, terminology (linguistic level, semantic level, contextual level)

– Consistency– Efficiency (time used, user satisfaction)– Use (tags selected, clouds consulted, order of consultation)

Improve retrieval– Effectiveness (degree of match between user and system

terminology)

In two different contexts: – Tagging by readers – Tagging by authors

Testing ApproachMain focus:

– free tagging with no instructionsVersus

– tagging using a combined system and guidance for users

Two demonstratorsIntute digital collection http://www.intute.ac.uk

– Major development– Tagging by reader– DDC

STFC repository http://epubs.cclrc.ac.uk/– Complementary development– Tagging by author– A more qualitative approach

Intute

Intute demonstrator: searching

Intute demonstrator : basic tagging

Intute demonstrator: enhanced tagging

EnTag: Intute user study (II)

Test setting– 50 graduate students in political science– 60 documents, covering up to four topics

of relevance for the students

Data collection– Logging time spent, selection patterns, – Pre- and post-questionnaires

EnTag: Intute user study (I)

Test: comparison of basic and advanced system:– Indexing– Perspective, specificity, exhaustivity– Linguistics (word class, single word/compound,

spelling, language)– Consistency– Efficiency (time used, user satisfaction)– Use (tags selected, clouds consulted, order of

consultation)– Retrieval efficiency

Degree of match between user and system terminology

– user tags, DDC tags, controlled Intute keywords, title terms, text terms

STFC Case Study: EPubs

STFC demonstrator

STFC Author study

A study on a Authors of papers– Smaller number - c.10-12. – Regular depositors ( > 10 papers each)– Subject experts

Expect that they would want their papers accurately tagged so that they are precisely found

A more qualitative study

Expected Feedback

Relative value of tagging vs. controlled terms– Does it give more satisfactory (accurate,

consistent) tags?– Does it lead to the consideration of tags they

would not have thought of?– Do they select deeply in the hierarchy?– Is this something they would like to see supported

more, and would use?– Is it worth the overhead?

How we should use a combination of tagging and controlled vocab in our system ?

To Be Continued…..

Building a Web of Knowledge

Social tagging and controlled vocabulary complement each other

– Tagging entry level, quick, does the job, but error prone, fuzzy

– Controlled vocabulary, accurate, but slow and expensive

Use one to leverage the other Use both to build a “Web of knowledge”

– The things in the world and their link via their subjects

– Get the users to build the means of organising the knowledge

http://purl.org/net/aliman 30

SKOS: Simple conceptual relationships

Conclusions

Controlled vocabulary and Tags complement each other

Hope to get some interesting evidence over the next month as the studies are complete.

Web 2.0 world offers the possibility of combining these results

– SKOS a format to use both tags and controlled vocabulary as part of the Web of Linked Data

– Also use Web 2.0 to build the vocab themselves.

Questions?

b.m.matthews@rl.ac.uk