Improving folksonomies quality by syntactic tag variations grouping

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

doi:10.1145/1529282.1529559

Cited by 4 publications

(6 citation statements)

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“…The fuzzy similarity measure provides good clustering rates of tag including syntactic variations [15]. Considering the related set of 1,000 annotations, the fuzzy similarity measure provides a correct classified rate (OK) of 91.4% for a threshold value of 0.0003.…”

Section: Resultsmentioning

confidence: 99%

“…In [15] we proposed a method that allowed the classification of tags (containing syntactic variations) based on a discriminator which computed similarity measures among a candidate tag and a set of pattern tags contained in a dictionary. The main drawback of similarity measures based on dictionary comparisons is their poor performance when considering short length chains.…”

Section: Syntactic Variations Clusteringmentioning

confidence: 99%

“…Another way to represent these variations is presented in [1,14]. The use of pattern matching techniques designed to automatically recognize syntactic variations of tags provides mechanisms to improve the quality of folksonomies [15]. Approximate string matching techniques allow dealing with the problem introduced by syntactic variations on folksonomies.…”

Section: Introductionmentioning

confidence: 99%

“…Imperfect pattern matching techniques perform better when dealing with syntactic variations of tags as indicated in [17]. The use of a fuzzy automaton with ε-moves (FA ε ), as described in [15], allows obtaining correct tag clustering rates greater than 95% when considering large data sets.…”

Section: Introductionmentioning

confidence: 99%

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A Tag Clustering Method to Deal with Syntactic Variations on Collaborative Social Networks

Astráin

Echarte

Córdoba

et al. 2009

Lecture Notes in Computer Science

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Folksonomies have emerged as a common way of annotating and categorizing content using a set of tags that are created and managed in a collaborative way. Tags carry the semantic information within a folksonomy, and provide thus the link to ontologies. The appeal of folksonomies comes from the fact that they require a low effort for creation and maintenance since they are community-generated. However they present important drawbacks regarding their limited navigation and searching capabilities, in contrast with other methods as taxonomies, thesauruses and ontologies. One of these drawbacks is an effect of its flexibility for tagging, producing frequently multiple syntactic variations of a same tag. Similarity measures allow the correct identification of tag variations when tag lengths are greater than five symbols. In this paper we propose the use of cosine relatedness measures in order to cluster tags with lengths lower or equal than five symbols. We build a discriminator based on the combination of a fuzzy similarity and a cosine measures and we analyze the results obtained.

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Section: Resultsmentioning

confidence: 99%

Section: Syntactic Variations Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Tag Clustering Method to Deal with Syntactic Variations on Collaborative Social Networks

Astráin

Echarte

Córdoba

et al. 2009

Lecture Notes in Computer Science

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“…This problem can be rectified by identifying all of them as variations of the interchangeable label ‘semantic web’ and then group them under a single tag. The user could access this tag and would be able to obtain all the information relating to the resources associated with it and its syntactic variations (Echarte et al ., 2009). Tag clouds, for example, could show a representative of each group of related variations and provide a doorway to all resources annotated with them.…”

Section: Tag Management Features Provided By the Folksonomy Applicationsmentioning

confidence: 99%

Quality-protected folksonomy maintenance approaches: a brief survey

Jabeen

Khusro

2015

The Knowledge Engineering Review

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Folksonomy gives liberty to its users to freely assign chosen keywords as tags, and this is the main reason behind its popularity. Apart from freedom, this system also reflects the collective intelligence of the crowd. However, this freedom and liberty can degrade quality of the folksonomy. It is required that quality of the folksonomy must remain consistently excellent and does not degrade with the passage of time. This is a survey paper, in which we present a brief survey of the research efforts intended to maintain a quality-protected folksonomy. We have organized our paper by looking at the problem from four aspects namely selection of quality tags, tag management features provided by folksonomy applications, folksonomy cleaning and interoperability of tags across platforms. We conclude our review with some of the interesting research topics, which need to be explored further. Our conclusion will be relevant and beneficial for engineers and designers who aim to design and maintain a quality-protected folksonomy.

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