Mining Association Rules in Folksonomies

Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd

doi:10.1007/3-540-34416-0_28

Cited by 128 publications

(91 citation statements)

References 12 publications

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“…A social tagging system often provides tag recommendation mechanisms (generally based on co-occurrence) that are used both to facilitate the identification of appropriate tags for a resource and to encourage consolidation of the tagging vocabulary across users. Recent work on more specialized topics, such as structure mining of user-generated vocabularies, has attempted to visualize trends (Dubinko et al, 2006), to identify patterns (Schmitz, Hotho, Jaschke, & Stumme, 2006) in tagging behavior, or to rank terms in a vocabulary . Xu, Fu, Mao, and Su (2006) introduced a collaborative approach to tag suggestion that is based on the HITS algorithm (Kleinberg, 1999) and computes a measure of the goodness of tags that are iteratively adjusted using a reward-penalty algorithm based on collective use.…”

Section: Approaches To Taggingmentioning

confidence: 99%

Perspectives on social tagging

Ding

Jacob

Zhang

et al. 2009

J. Am. Soc. Inf. Sci.

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Social tagging is one of the major phenomena transforming the World Wide Web from a static platform into an actively shared information space. This paper addresses various aspects of social tagging, including different views on the nature of social tagging, how to make use of social tags, and how to bridge social tagging with other Web functionalities; it discusses the use of facets to facilitate browsing and searching of tagging data; and it presents an analogy between bibliometrics and tagometrics, arguing that established bibliometric methodologies can be applied to analyze tagging behavior on the Web. Based on the Upper Tag Ontology (UTO), a Web crawler was built to harvest tag data from Delicious, Flickr, and YouTube in September 2007. In total, 1.8 million objects, including bookmarks, photos, and videos, 3.1 million taggers, and 12.1 million tags were collected and analyzed. Some tagging patterns and variations are identified and discussed.

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Section: Approaches To Taggingmentioning

confidence: 99%

Perspectives on social tagging

Ding

Jacob

Zhang

et al. 2009

J. Am. Soc. Inf. Sci.

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“…Recently, work on more specialized topics such as structure mining on folksonomiese. g. to visualize trends [5] and patterns [16] in users' tagging behavior-as well as ranking of folksonomy contents [9], analyzing the semiotic dynamics of the tagging vocabulary [3], or the dynamics and semantics [6] have been presented.…”

Section: A Formal Model For Folksonomiesmentioning

confidence: 99%

Tag Recommendations in Folksonomies

Jäschke

Marinho

Hotho

et al.

Lecture Notes in Computer Science

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333

286

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Abstract. Collaborative tagging systems allow users to assign keywords-so called "tags"-to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.In this paper we evaluate and compare two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graphbased recommender outperforms existing methods considerably.

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“…weighted tripartite graph in Section 3.1). Others apply association rule mining to detect super-sub-concept relations [16]. Given tags from Flickr, Schmitz detects tag pairs where one tag subsumes the other [17].…”

Section: Learning Tag Relationsmentioning

confidence: 99%

The Impact of Multifaceted Tagging on Learning Tag Relations and Search

Abel

Henze

Kawase

et al. 2010

Lecture Notes in Computer Science

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Abstract. In this paper we present a model for multifaceted tagging, i.e. tagging enriched with contextual information. We present TagMe!, a social tagging front-end for Flickr images, that provides multifaceted tagging functionality: It enables users to attach tag assignments to a specific area within an image and to categorize tag assignments. Moreover, TagMe! maps tags and categories to DBpedia URIs to clearly define the meaning of freely-chosen words. Our experiments reveal the benefits of these additional tagging facets. For example, the exploitation of the facets significantly improves the performance of FolkRank-based search. Further, we demonstrate the benefits of TagMe! tagging facets for learning semantics within folksonomies.

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Mining Association Rules in Folksonomies

Cited by 128 publications

References 12 publications

Perspectives on social tagging

Perspectives on social tagging

Tag Recommendations in Folksonomies

The Impact of Multifaceted Tagging on Learning Tag Relations and Search

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