Instancebased mapping between thesauri and folksonomies

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Instance-based mapping between thesauri and
folksonomies

• Christian Wartena
• Rogier Brussee
• Telematica Instituut

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Outline

• Interoperability of Keywords
• Wikipedia and del.icio.us
• Keyword similarity
• Experiment
• Conclusion

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Interoperability of Keywords

• Documents (pictures, movies, ) are annotated
  with keywords for organization and retrieval.
• In different collections/communities different
  sets of keywords are used.
• The set of selectable keywords is often organized
  in and delimited by a thesaurus.
• The set of freely generated end-user keywords,
  tags forms a folksonomy
• Align keywords/tags by comparing usage.
• Tested on del.icio.us tags and Wikipedia
  categories.

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del.icio.us and Wikipedia

• Del.icio.us
• Social book marking site
• Bookmarks in most cases can be interpreted as
  labels or tags for the bookmarked URL.
• Many Wikipedia articles are tagged by del.icio.us
  users
• Wikipedia
• Articles are labeled with one or more categories
  by the article authors.
• Categories are organized hierarchically.
• Categories are organized consciously like in a
  thesaurus
• New categories are introduced after discussions
  between active Wikipedians.

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Keyword alignment

• Problem
• Given a keyword k in a system A, what is the most
  similar keyword k in system B.
• Given a tag from del.icio.us, what is the most
  similar Wikipedia category (or vice versa).
• Approach
• Interpret similarity as similarity of usage.
• Compute similarity of usage on a common
  sub-collection.
• Evaluation
• Compare results to human judgment of similarity.

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Keyword similarity

• Basic assumption similarity is similarity of
  usage.
• If two keywords have similar usage they will give
  similar results in retrieval tasks.
• Two keywords have similar usage if they
• Have a similar distribution over documents
• Divergence (relative entropy) of distributions
• Cosine
• Often co-occur
• Jaccard coefficient

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New measure for keyword similarity

• Keywords have similar usage if they co-occur with
  similar frequency with all other keywords.
• We use the frequency with which a tag/keyword is
  assigned to a document.
• We include co-occurrence information with other
  terms.
• Helps to cope with sparse data
• In other words
• Terms are similar if they have similar
  co-occurrence patterns
• Similar to Tag Context Similarity of Cattuto et
  al.s presentation tomorrow (Semantic Social
  Networks Session)

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Formalization Distribution of co-occurring terms

• where
• q(td) is the keyword distribution of d
• Q(dz) is the document distribution of z
• The fraction of zs that is found in d
• Weighted average of the keyword distributions of
  documents
• The weight is the relevance of d for z given by
  the probability Q(dz)

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Distance of keywords

• For each keyword there is a distribution over all
  (other) keywords.
• Similarity is expressed by divergence of these
  distributions
• Kullback-Leibler divergence
• Bits per keyword saved by compressing a
  subcollection with keyword distribution p using p
  instead of a general distribution q.

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Distance of keywords (contd)

• Jensen-Shannon divergence
• Mean distribution
• Jensen-Shannon divergence is symmetric.
• Jensen-Shannon divergence is square of a
  non-negative distance satisfying the triangle
  inequality.

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Alignment

• Consider a collection of documents annotated with
  different sets of keywords.
• Represent a keyword by a distribution over terms
  from both collections.
• For each term find the closest term from the
  other collection.

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Experiment I

• Mapping between Teleblik keywords and User Tags
• Educational videos.
• Professional keywords from public broadcasting
  archive.
• Keywords assigned in an experiment by high school
  students.
• Data
• 100 videos
• 12.414 tags
• 4.348 different tags
• 269 different keywords

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Experiment II

• Mapping between del.icio.us tags and Wikipedia
  categories
• Del.icio.us tags collected by Mathias Lux
  (Klagenfurt Univ.)
• Data
• 58.345 Wikipedia articles
• 500.618 tags and category annotations
• 42.425 different Wikipedia categories
• 49.603 different tags
• Mappings computed for tags occurring on at least
  10 docs.
• Mappings for 2355 tags
• Mappings for 1827 categories
• Using co-occurrence data with all 49.603
  tags/categories

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Evaluation of mapping

• Manual evaluation
• Classification of a sample of mappings into
• Broader term
• Narrower
• Related term
• Unrelated
• Source term is not a keyword (e.g. to read)
• Meaning unknown

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Evaluation of aligning Wikipedia and del.icio.us
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Distance vs. mapping quality

• Pairs with a small distance are evaluated better
  than pairs with large distance.
• Evaluation of mappings with smallest and largest
  distance
• a) Categories to tags
• b) Tags to categories

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Effect of keyword frequency

• No correlation between keyword frequency and
  divergence with best mapping found.

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Comparison with Jaccard-coefficient

• Evaluation of mapping using two different
  distance measures.
• Categories broader, narrower and related are
  merged
• Results for
• a) Categories to tags
• b) Tags to categories

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Discussion of results

• Method works very well in test
• Good mapping results
• Distance is good indication of quality
• Insensitive to frequency (upto a certain degree)
• Better than Jaccard, because it uses
• co-occurrence with other tags (tag context)
• frequency with which a tag is assigned to a
  document.
• Frequency information is typical for user
  generated tags.
• We expect this method to perform less well for
  aligning keywords with other keywords (without
  assignment frequencies).
• Distance measure also works well for clustering
  tags.

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Future work

• Evaluating relatedness using external sources
  (e.g. Wordnet)
• Compare to other distance measures
• We used documents annotated completely according
  to two annotation schemes.
• How large has the overlap to be to obtain decent
  results?
• We can create partial overlap of disjoint
  document sets by a partial identification of the
  keywords.
• Detect asymmetry in relations (broader vs.
  narrower term)

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Conclusion

• Using co-occurrence patterns is a fruitful
  approach.
• Frequent terms from folksonomies do behave
  similar to carefully assigned keywords.
• Because usage based similarity measure yields
  good mappings.
• Folksonomy seems to work!