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Hayman, S.; Lothian, N.: Taxonomy directed folksonomies : integrating user tagging and controlled vocabularies for Australian education networks (2007)
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- Abstract
- What is the role of controlled vocabulary in a Web 2.0 world? Can we have the best of both worlds: balancing folksonomies and controlled vocabularies to help communities of users find and share information and resources most relevant to them? education.au develops and manages Australian online services for education and training. Its goal is to bring people, learning and technology together. education.au projects are increasingly involved in exploring the use of Web 2.0 developments building on user ideas, knowledge and experience, and how these might be integrated with existing information management systems. This paper presents work being undertaken in this area, particularly in relation to controlled vocabularies, and discusses the challenges faced. Education Network Australia (edna) is a leading online resource collection and collaborative network for education, with an extensive repository of selected educational resources with metadata created by educators and information specialists. It uses controlled vocabularies for metadata creation and searching, where users receive suggested related terms from an education thesaurus, with their results. We recognise that no formal thesaurus can keep pace with user needs so are interested in exploiting the power of folksonomies. We describe a proof of concept project to develop community contributions to managing information and resources, using Taxonomy-Directed Folksonomy. An established taxonomy from the Australian education sector suggests terms for tagging and users can suggest terms. Importantly, the folksonomy will feed back into the taxonomy showing gaps in coverage and helping us to monitor new terms and usage to improve and develop our formal taxonomies. This model would initially sit alongside the current edna repositories, tools and services but will give us valuable user contributed resources as well as information about how users manage resources. Observing terms suggested, chosen and used in folksonomies is a rich source of information for developing our formal systems so that we can indeed get the best of both worlds.
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Spiteri, L.: ¬The structure and form of folksonomy tags : the road to the public library catalogue (2007)
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Furner, J.: Folksonomies (2009)
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Peters, I.: Folksonomies und kollaborative Informationsdienste : eine Alternative zur Websuche? (2011)
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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)
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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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Peters, I.: Folksonomies : nutzergenerierte Schlagwörter als Indexierungswerkzeug für die Massen (2011)
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Peters, I.; Schumann, L.; Terliesner, J.: Folksonomy-basiertes Information Retrieval unter der Lupe (2012)
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Peters, I.: Folksonomies & Social Tagging (2023)
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Güntner, G.; Sint, R.; Westenthaler, R.: ¬Ein Ansatz zur Unterstützung traditioneller Klassifikation durch Social Tagging (2008)
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Fiala, S.: Deutscher Bibliothekartag Leipzig 2007 : Sacherschließung - Informationsdienstleistung nach Mass (2007)
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