Semantics discovery in social tagging systems: A review

Jabeen, Fouzia; Khusro, Shah; Majid, Amna; Rauf, Abdul

doi:10.1007/s11042-014-2309-3

Cited by 21 publications

(8 citation statements)

References 86 publications

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“…On the other hand, Xie et al [42] extract communities of similar users from their tagging profiles to enrich future tag suggestions. Jabeen et al [22] provide a comprehensive review of approaches to extracting semantics from social tagging datasets.…”

Section: Related Workmentioning

confidence: 99%

Limiting Tags Fosters Efficiency

Santos

Burghardt

Lerman

et al. 2021

13th ACM Web Science Conference 2021

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Tagging facilitates information retrieval in social media and other online communities by allowing users to organize and describe online content. Researchers found that the efficiency of tagging systems steadily decreases over time, because tags become less precise in identifying specific documents, i.e., they lose their descriptiveness. However, previous works did not answer how or even whether community managers can improve the efficiency of tags. In this work, we use information-theoretic measures to track the descriptive and retrieval efficiency of tags on Stack Overflow, a question-answering system that strictly limits the number of tags users can specify per question. We observe that tagging efficiency stabilizes over time, while tag content and descriptiveness both increase. To explain this observation, we hypothesize that limiting the number of tags fosters novelty and diversity in tag usage, two properties which are both beneficial for tagging efficiency. To provide qualitative evidence supporting our hypothesis, we present a statistical model of tagging that demonstrates how novelty and diversity lead to greater tag efficiency in the long run. Our work offers insights into policies to improve information organization and retrieval in online communities.

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Section: Related Workmentioning

confidence: 99%

Limiting Tags Fosters Efficiency

Santos

Burghardt

Lerman

et al. 2021

13th ACM Web Science Conference 2021

View full text Add to dashboard Cite

show abstract

“…Much work has been done to introduce semantics in folksonomy [16][17][18][19], and to investigate methods of deploying this semantics for tasks such as information retrieval [20][21][22], recommender systems [23][24][25][26], and ontologies development [27][28][29]. As well, quite a number of works has been done to extract structured knowledge and develop ontologies from social tagging systems.…”

Section: Related Workmentioning

confidence: 99%

Enabling social WEB for IoT inducing ontologies from social tagging

Alruqimi¹,

Aknin²

2019

IJ-ICT

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<span>Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span>

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“…The work by Gupta et al [2010] discusses papers on why people tag, what influences the choice of tags, and how to model the tagging process, but its discussion on content-based image tagging is limited. The focus of Jabeen et al [2016] is on papers about adding semantics to tags by exploiting varied knowledge sources such as Wikipedia, DBpedia, and WordNet. Again, it leaves the visual information untouched.…”

Section: Scope Aims and Organizationmentioning

confidence: 99%

Socializing the Semantic Gap

et al. 2016

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Where previous reviews on content-based image retrieval emphasize what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems (i.e., image tag assignment, refinement, and tag-based image retrieval) is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, that is, estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this article introduces a two-dimensional taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison with the state of the art, a new experimental protocol is presented, with training sets containing 10,000, 100,000, and 1 million images, and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.

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Semantics discovery in social tagging systems: A review

Cited by 21 publications

References 86 publications

Limiting Tags Fosters Efficiency

Limiting Tags Fosters Efficiency

Enabling social WEB for IoT inducing ontologies from social tagging

Socializing the Semantic Gap

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