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Mining a Web 2.0 service for the discovery of semantically similar terms:

A case study with Del.icio.us

Kwan YiSchool of Library and Information Science

College of Communications and Information StudiesUniversity of Kentucky

Social bookmarking: Del.icio.us

• Del.icio.us is one of most popular social bookmarking systems:–3 million registered users and–100 million unique URLs bookmarked,

as of September 2007

Folksonomy

• We define folksonomy as a collective set of tags (keywords or terms) assigned by participants in a social tagging system.–User-created vocabulary–Uncontrolled vocabulary–Built in a collaborative manner

Example: A folksonomy in Delicious.com

Resource titleResource title

Resource taggersResource taggers

Resource URLResource URL

Tagging historyTagging history

Popular tagsPopular tags

Objective of the Study

• To examine an effective way of mining semantically similar terms from folksonomy for the purpose of investigating the feasibility of folksonomy as a potential data source of semantically similar terms

Proposed algorithms for mining similar terms from Folksonomy

• Co-occurrence-based similarity algorithm

• Correlation-based similarity algorithm

Experiment (I)

• To identify similar terms of each of the 121 most popular tags on Del.icio.us posted on the fifteenth of May 2008

Result: How many similar terms for the 121 popular tags?

• Co-occurrence-based algorithm– 2.6 similar terms (Level of similarity = 0.9)– 5.1 similar terms (Level of similarity = 0.7)– 10.1 similar terms (Level of similarity = 0.5)

• Correlation-based algorithm– 0.9 similar terms (Level of similarity = 0.9)– 1.6 similar terms (Level of similarity = 0.7)– 2.6 similar terms (Level of similarity = 0.5)

Experiment (II)

• To identify similar terms of each of the 32 tags (out of the 121) that are not listed on the online version of Merriam-Webster Dictionary

Result: How many similar terms for the 32 not-in-the-dictionary tags?

• Co-occurrence-based algorithm– 3.3 similar terms (Level of similarity = 0.9)– 5.9 similar terms (Level of similarity = 0.7)– 10.1 similar terms (Level of similarity = 0.5)

• Correlation-based algorithm– 1 similar terms (Level of similarity = 0.9)– 1.7 similar terms (Level of similarity = 0.7)– 2.4 similar terms (Level of similarity = 0.5)

Webdesign(similarity level: 0.9)

• Co-occurrence [12]:

resources css web design reference html tutorial tutorials inspiration gallery development webdev

• Correlation [4]:

css design html inspiration

Findings

• The correlation-based is more selective than the co-occurrence-based.• The co-occurrence-based appears to

be most attractive with the similarity level of 0.7.

Conclusion

• As social bookmarking systems are more popularly utilized, the potential of their folksonomies for the mining task will be more increased.

Thanks!

Co-occurrence-based similarity algorithm (Identifying similar terms of the term W)

W (100)A (50)B (20)C (10)

W (87)B (57)C (40)A (30)

W (1032)A (250)F (120)D (78)

W (37)A (29)B (16)F (9)

A (4)B (3)C (2)F (2)D (1)

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22

CoSA(s=1: A W)

CoSA(s=0.75: B W)

CoSA(s=0.5: C W)

CoSA(s=0.5: F W)

33

CoSA(s=0.25: D W)

Correlation-based similarity algorithm

• Term X is said to be similar to term W on the basis of the correlation-based algorithm: CrSA(s: XW)

• CrSA(s: XW) can be defined only if both CoSA(s: XW) and CoSA(s: WX) are satisfied.