Search (3 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Folksonomies"
  1. Peters, I.: Folksonomies und kollaborative Informationsdienste : eine Alternative zur Websuche? (2011) 0.00 
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    Pages
    S.29-53
  2. Moreiro-González, J.-A.; Bolaños-Mejías, C.: Folksonomy indexing from the assignment of free tags to setup subject : a search analysis into the domain of legal history (2018) 0.00 
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    Abstract
    The behaviour and lexical quality of the folksonomies is examined by comparing two online social networks: Library-Thing (for books) and Flickr (for photos). We presented a case study that combines quantitative and qualitative elements, singularized by the lexical and functional framework. Our query was made by "Legal History" and by the synonyms "Law History" and "History of Law." We then examined the relevance, consistency and precision of the tags attached to the retrieved documents, in addition to their lexical composition. We identified the difficulties caused by free tagging and some of the folksonomy solutions that have been found to solve them. The results are presented in comparative tables, giving special attention to related tags within each retrieved document. Although the number of ambiguous or inconsistent tags is not very large, these do nevertheless represent the most obvious problem to search and retrieval in folksonomies. Relevance is high when the terms are assigned by especially competent taggers. Even with less expert taggers, ambiguity is often successfully corrected by contextualizing the concepts within related tags. A propinquity to associative and taxonomic lexical semantic knowledge is reached via contextual relationships.
  3. Xie, H.; Li, X.; Wang, T.; Lau, R.Y.K.; Wong, T.-L.; Chen, L.; Wang, F.L.; Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy (2016) 0.00 
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    Abstract
    In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.

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