Exploring social annotations for the semantic web

Wang, Xian; Zhang, Lei; Yu, Yong

doi:10.1145/1135777.1135839

Cited by 287 publications

(180 citation statements)

References 28 publications

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“…Aquest coneixement reflecteix les opinions independents i diverses d'un grup de persones (Sunstein 2006). Les etiquetes d'aquesta folksonomy (Angeletou et al 2007) estan sovint força relacionades entre si, ja que poden ser utilitzades per un usuari per etiquetar recursos relacionats, o per diferents usuaris per etiquetar el mateix recurs (Wu, Zhang, and Yu 2006;Campbell 2006). Els patrons que sorgeixen d'aquestes pràctiques d'etiquetatge proporcionen, no només una manera d'organitzar la informació per als usuaris (Kipp and Campbell 2006), sinó també una capa de semàntica feble (Dye 2006) …”

Section: Feina Relacionadaunclassified

Effective Use of Ontologies

Conesa¹,

Roma²

2012

IN3 work. pap. ser.

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Section: Feina Relacionadaunclassified

Effective Use of Ontologies

Conesa¹,

Roma²

2012

IN3 work. pap. ser.

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“…Another piece of work that is related to ours is introduced in [30]. This work applies a separable mixture model to demonstrate emergent semantics in a social tagging system.…”

Section: Related Workmentioning

confidence: 99%

CubeLSI: An effective and efficient method for searching resources in social tagging systems

Lee

Kao

et al. 2011

2011 IEEE 27th International Conference on Data Engineering

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In a social tagging system, resources (such as photos, video and web pages) are associated with tags. These tags allow the resources to be effectively searched through tag-based keyword matching using traditional IR techniques. We note that in many such systems, tags of a resource are often assigned by a diverse audience of causal users (taggers). This leads to two issues that gravely affect the effectiveness of resource retrieval: (1) Noise: tags are picked from an uncontrolled vocabulary and are assigned by untrained taggers. The tags are thus noisy features in resource retrieval. (2) A multitude of aspects: different taggers focus on different aspects of a resource. Representing a resource using a flattened bag of tags ignores this important diversity of taggers. To improve the effectiveness of resource retrieval in social tagging systems, we propose CubeLSI -a technique that extends traditional LSI to include taggers as another dimension of feature space of resources. We compare CubeLSI against a number of other tag-based retrieval models and show that CubeLSI significantly outperforms the other models in terms of retrieval accuracy. We also prove two interesting theorems that allow CubeLSI to be very efficiently computed despite the much enlarged feature space it employs.

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“…One of such approach is to build emergent semantics from social web which aims to overcome the knowledge acquisition bottleneck. In [14] and [27], it has been shown that light weight ontologies can be constructed from the social tagging behaviors.…”

Section: Related Workmentioning

confidence: 99%