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Collaborative Tagging & Folksonomy

Folksonomy is a neologism of two words, ’folk’ and ’taxonomy’ which describes conceptual structures created by users [4, 5]. A folksonomy is a set of unstructured collaborative usage of tags for content classification and knowledge representation that is popularized by Web 2.0 and social applications [1, 5]. Unlike taxonomy that is commonly used to organize resources to form a category hierarchy, folksonomy is non-hierarchical and non-exclusive [3]. Both content hierarchy and folksonomy can be used together to better content classification.

Users of Web 2.0 applications typically organize their created content with a set of terms or keywords, also commonly known as tags [3, 4, 6, 7]. Spending additional time and effort to provide annotation such as tags to a resource usually implicitly implies the relevance and/or importance of the resource to a user [1]. Therefore, besides being used for content classification, tags assignment can also be analyze to find the implicit relationship between users and the tagged content.

It is also observed that the frequency of term usage is proportional to the level of importance or connection to user, i.e. the more a tag is used, the more it is important to the user [1, 8]. The choice of tags when annotating a resource often express the user interest and perceptions [8]. Users usually have different ideas and interests when tagging a resource especially when the application allow the same resource tagged by multiple users [2]. Therefore tagging activity pattern can be studied to find a user’s domain of interest.
Users with common interests tend to use similar set of tags in tagging re- sources of interest [2]. The similarity of users can then be calculated through the tagging pattern for recommending purpose as it provides social relationships between users [4, 7]. This may allow more collaboration between users sharing similar interest.

Although the variety selection of tag helps in better representation of the resource, but this may cause some problems as users don’t tend to agree to each other [2]. Observations also shows tagging behaviour follows power law distribution where tags are mostly assigned to a small subset of more
popular resources [2, 7]. Hence, when a resource gets popular, users having different interests will attempt to assign tags with keywords from all different domains. However, when different tags that are semantically related from each other are assigned to related resources (vocabulary mismatch problem), it may drastically decrease the level of relevance although the tags may actually share similar meaning [3, 7].

Correlation between resources is often established when they are assigned with common tags [8]. The level of relevance can then be observed by studying the similarity in their set of assigned tags [8]. The correlation and similarity between the tags assigned to resources may also help users to find unseen resources by browsing the tags.

The assigned tags can be used as a tool to enable discovery, sharing and collaboration of web resources [1, 2, 3, 8]. Some applications even allow sharing of tagging information [2, 8]. Hence, besides using tags as a tool for content classification and user interest study, they can be used to make collaboration easier.

A resource may be described by its assigned tags depending on the tagging behaviour [1]. When an tagging system follows the bag-model, i.e. system that allows multiple users to tag a resource, the frequency of a tag is assigned also indirectly shows the increased relevance between the tag and the resource [1, 2, 8]. Weights can be also calculated for each tag to rank the relevance with the resource.
On the other hand, systems that implements the set-model do not provide enough data to deduce the level of importance or popularity of an assigned tag to the resource [8]. Due to the unstructured nature of collabo- rative tagging, the system can be easily abused if users assign tags that do not describe the content [1, 8]. The problem is more apparent for system implementing the set-model as there is no way to rank the assigned tags.

References

  1. I. Cantador, A. Bellog ́ın, and D. Vallet. Content-based Recommendation in Social Tagging Systems. Methodology, pages 237–240, 2010.
  2. Y. Guo and J. B. Joshi. Topic-based personalized recommendation for collaborative tagging system. Number 10. ACM Press, New York, New York, USA, 2010.
  3. R. Lambiotte and M. Ausloos. Collaborative tagging as a tripartite network, 2005.
  4. P. Mika. Ontologies are us: A unified model of social networks and semantics. Web Semantics Science Services and Agents on the World Wide Web, 5(1):5–15, 2007.
  5. C. Schmitz, A. Hotho, R. J ̈aschke, and G. Stumme. Mining Association Rules in Folksonomies, pages 261–270. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, 2006.
  6. N. Shadbolt, T. Berners-Lee, and W. Hall. The Semantic Web Revisited. IEEE Intelligent Systems, 21(3):96–101, May 2006.
  7. S. Siersdorfer and S. Sizov. Social recommender systems for web 2.0 folksonomies. ACM Press, New York, New York, USA, 2009.
  8. M. Szomszor, C. Cattuto, H. Alani, K. OHara, A. Baldassarri, V. Loreto, and V. D. P. Servedio. Folksonomies, the Semantic Web, and Movie Recommendation. eprintsecssotonacuk, pages 71–84, 2007.
I initially wrote this for my report, but thought this may make a good blog post, hence posting it over.

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