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Enhancing social tagging with a knowledge
organization system
Brian MatthewsSTFC
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Outline
Who are STFC ? Controlled Vocabulary Social Tagging EnTag
– Aims– Glamorgan/UKOLN/Intute Experiment– STFC Experiment
SKOS
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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
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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
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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
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HASSET (II)
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HASSET (III)
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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
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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.
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Connotea
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Connotea – sharing tags
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Connotea –Tag Cloud
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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
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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/
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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
•
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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
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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
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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
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Intute
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Intute demonstrator: searching
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Intute demonstrator : basic tagging
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Intute demonstrator: enhanced tagging
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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
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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
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STFC Case Study: EPubs
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STFC demonstrator
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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
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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…..
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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
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http://purl.org/net/aliman 30
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SKOS: Simple conceptual relationships
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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.