Pattern Matching Techniques to Identify Syntactic Variations of Tags in Folksonomies

Echarte, Francisco; Astráin, José Javier; Córdoba, Alberto; Villadangos, J.

doi:10.4018/978-1-60566-272-5.ch011

Cited by 8 publications

(12 citation statements)

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“…Echarte, et al, [2] discuss the problem of syntactic variations in folksonomies. They propose the utilization of pattern matching techniques to identify syntactic variations of tags.…”

Section: Tag Clusteringmentioning

confidence: 99%

“…Inside the cluster is dense and outside the cluster is sparse, which is a n important property of good clustering. We can also use Entropy [4] to evaluate the result of clustering: (2) n: number of clusters in set sample X pt: probability of items in i cluster appearing in X The bigger the entropy, the more the set sample is dispersed. When the probability of items in n clusters appearing in X is the same, the Entropy will get the biggest value log2 (n).…”

Section: Clustering Validitymentioning

confidence: 99%

See 1 more Smart Citation

A New Clustering Model Based on Word2vec Mining on Sina Weibo Users' Tags

Xue¹,

Fu²,

Shaobin³

2014

IJGDC

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“…Echarte, et al, [2] discuss the problem of syntactic variations in folksonomies. They propose the utilization of pattern matching techniques to identify syntactic variations of tags.…”

Section: Tag Clusteringmentioning

confidence: 99%

Section: Clustering Validitymentioning

confidence: 99%

A New Clustering Model Based on Word2vec Mining on Sina Weibo Users' Tags

Xue¹,

Fu²,

Shaobin³

2014

IJGDC

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show abstract

“…However, the free‐form nature of tagging also leads to a number of vocabulary problems. Multiple reasons can caused tag variations : inconsistent grouping of compound words consisting of more than two words. Often, users insert punctuation to separate the words, for example, ancient‐egypt , ancient _ egypt , and ancientgypt ; use of symbols in tags; symbols such as #, ‐, +, /, :, _, &, and !…”

Section: Social‐based Classificationmentioning

confidence: 99%

One‐class support vector machines for personalized tag‐based resource classification in social bookmarking systems

Godoy

2012

Concurrency and Computation

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SUMMARY Social tagging systems allow users to easily create, organize, and share collections of Web resources in a collaborative fashion. Videos, pictures, research papers, and Web pages are shared and annotated in sites such as Del.icio.us, CiteULike, or Flickr, among others. The rising popularity of these systems leads to a constant increase in the number of users actively publishing and annotating resources and, consequently, an exponential growth in the amount of data contained in their folksonomies, the underlying data structure of tagging systems. In turn, the user task of discovering interesting resources becomes more and more difficult and time‐consuming. In this paper, the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One‐class support vector machine classification is evaluated as a means to identify relevant information for users based exclusively on positive examples of their information preferences. It is assumed that users express their interest on resources belonging to a folksonomy by assigning tags to them, whereas there is no straightforward method to collect uninterestingness judgments. Filtering interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities of Web communities. In this paper, the results achieved with tag‐based classification are compared with those obtained using more traditional information sources such as the full text of Web pages. Experimental evaluation showed that tag‐based classifiers outperformed those learned using the text of documents as well as other content‐related sources. Moreover, tag‐based classification becomes essential for folksonomies in which no additional content is available because of the nature of resources being stored (e.g., tagging of photos or videos). Copyright © 2012 John Wiley & Sons, Ltd.

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“…To investigate the role social tagging can play in Web page categorization we performed an empirical analysis using Social-ODP-2k9 [13] dataset, which basically links Web pages with their corresponding categories in a Web directory such as the Open Directory Project (ODP) 4 and tags assigned to these pages by users in a popular social bookmarking site such as Del.icio.us.…”

Section: Empirical Evaluationmentioning

confidence: 99%

“…In consequence, to determine whether tags are a valuable source of information for classification of Web page some filtering techniques were considered and compared. Tags variations can be attributed to several factors [12,4]:…”

Section: Tag-based Classificationmentioning

confidence: 99%

Exploiting the Social Capital of Folksonomies for Web Page Classification

Godoy

Amandi

2010

IFIP Advances in Information and Communication Technology

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Abstract. Collaborative tagging systems (CTSs), also known as folksonomies, have grown in popularity on the Web and social tagging has become an important feature of many Web 2.0 services. It has been argued that the power of tagging lies in the ability for people to freely determine the appropriate tags for resources without having to rely on a predefined lexicon or hierarchy. The free-form nature of tagging causes a number of problems in this social classification scheme, such as synonymy and morphological variety. However, social tagging can be a valuable source of information to help in the organization of Web resources. In this paper we present an empirical analysis carried out to determine the importance of social tagging in Web page classification. Experimental results showed that tag-based classification outperformed classifiers based on full-text of documents.

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Pattern Matching Techniques to Identify Syntactic Variations of Tags in Folksonomies

Cited by 8 publications

References 0 publications

A New Clustering Model Based on Word2vec Mining on Sina Weibo Users' Tags

A New Clustering Model Based on Word2vec Mining on Sina Weibo Users' Tags

One‐class support vector machines for personalized tag‐based resource classification in social bookmarking systems

Exploiting the Social Capital of Folksonomies for Web Page Classification

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