Harvesting social knowledge from folksonomies

Wu, Hao; Zubair, Mohammad; Maly, Kurt

doi:10.1145/1149941.1149962

Cited by 114 publications

(77 citation statements)

References 3 publications

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“…Other work focuses on personal recommendations based on the relatedness between the tags, as in [ 21]; in our approach, we currently use the same technique; however for future versions, we plan to apply other adaptive learning strategies as described by the LAG language. Furthermore, in [ 1] user groups are created based on the user tags; whereas in our method, we allow the end users to have full control on creating/joining/leaving the groups based on the user's privileges within the system.…”

Section: Evaluations and Discussionmentioning

confidence: 99%

Social Reference Model for Adaptive Web Learning

Ghali

Cristea

2009

Advances in Web Based Learning – ICWL 2009

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Abstract. In this paper, we describe the design steps of extending LAOS, a five-layer framework for generic adaptive web learning authoring, by adding a social layer to capture (and adapt) information from 1) collaborative authoring (i.e. editing the content of other learners, describing the content using tags, rating the content, and commenting on the content, etc); and 2) authoring for collaboration (i.e., adding authors' activities, such as defining groups of authors, subscribing to other authors, etc). Moreover, the paper presents MOT 2.0, an adaptive E-learning 2.0 system, which is built on the proposed reference model, and finally, we report on our evaluations to validate the new Social Layer by comparing MOT 2.0 with its predecessor, MOT 1.0.

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Section: Evaluations and Discussionmentioning

confidence: 99%

Social Reference Model for Adaptive Web Learning

Ghali

Cristea

2009

Advances in Web Based Learning – ICWL 2009

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“…Folksonomy is created by open and uncontrolled systems (social tagging systems) where users can annotate resources with different tags depending on their social or cultural backgrounds, expertise and perception of the world (Belelman et al, 2006;Golder and Huberman, 2005;Peterson, 2006;Wu H. et al, 2006).…”

Section: Folksonomy and Ontologymentioning

confidence: 99%

“…It is a free-form annotation of web resources, done by users, and without the constraints of a predefined taxonomy or ontology (Wu H et al, 2006). Thus, in the Social Web, the meaning of the tagging data is completely unspecified.…”

Section: Folksonomy and Ontologymentioning

confidence: 99%

INTEGRATING SOCIAL WEB WITH SEMANTIC WEB - Ontology Learning and Ontology Evolution from Folksonomies

Freddo

Tacla

2009

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

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Abstract:In this paper, we present an approach for integrating Social Web with Semantic Web by combining the easiness of annotation of resources in the Social Web and the expressiveness of ontologies to describe the resources in the Semantic Web. Our approach combines ontology learning and ontology evolution techniques to provide an integrated Web. Besides, we show how ontology alignment can be used to enrich ontologies in this context.

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“…However while tagging systems claim to have many advantages over controlled vocabularies or taxonomies, they still suffer from some limitations, such as the lack of lexical relations [6] or information overload [7]. Searching and retrieving may turn very limited if we ignore lexical relations, especially in a system where many users with different backgrounds have added different tags.…”

Section: Motivationmentioning

confidence: 99%

Visual Tagging Through Social Collaboration: A Concept Paper

Bellucci

Levialdi

Malizia

2007

Lecture Notes in Computer Science

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Abstract. Collaborative tagging has grown on the Internet as a new paradigm for web information discovering, filtering and retrieval. In the physical world, we use visual tags: labels readable by smartphones with cameras. While visual tags are usually related to a web site address, collaborative tagging, instead, provides updated, recommended information contributed and shared by users. In this paper we investigate the combination of collaborative tagging systems with visual tags. We present a prototype of a semiautomatic system generating visual tags which gather information from collaborative tagging. The user can interact with a list of relevant tags (built by clustering closely related tags) that can be further encoded in a visual tag, according to user's preferences. The user experience is enriched by retrieving multimedia content linked to the selected tags, present on the web. We finally show a case study illustrating our approach.

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Harvesting social knowledge from folksonomies

Cited by 114 publications

References 3 publications

Social Reference Model for Adaptive Web Learning

Social Reference Model for Adaptive Web Learning

INTEGRATING SOCIAL WEB WITH SEMANTIC WEB - Ontology Learning and Ontology Evolution from Folksonomies

Visual Tagging Through Social Collaboration: A Concept Paper

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