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Collaborative tagging systems have recently emerged as a powerful way to label and organize large collections of data. The informal social classification structure in these systems, also known as folksonomy, provides a convenient way to annotate resources by allowing users to use any keyword or tag that they find relevant. Although folksonomies and...
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... the following, we use this running example to illustrate the proposed measure. For the folksonomy example in Figure 2, the macroaggregation representation is shown in Table 1 and Table 2 for the users Mohamed and Amine respectively. Taking users into account gives the emergent semantic learned from the folksonomy, the consensus characteristic which means more accurate ontology than when taking only resources into account. ...
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... Furthermore, these approaches do not give a formal solution to the disambiguation and context identification problem. Another stream of research associates semantic entities to tags as a way to formally define their meaning, but these approaches need existing ontologies that match well the folksonomy In this paper, we propose an improvement of our approach for extracting hierarchies from folksonomies previously introduced in [9]. An ameliorated similarity measure as well as a new algorithm for context identification and disambiguation are introduced. ...
... The authors add context identification and disambiguation tasks to the algorithm. A promising approach is presented in [9]. The authors propose a new similarity measure called CDU (Co-occurrences in Distinct Users), that exploits the three mode of the folksonomy. ...
Web 2.0 is an evolution toward a more social, interactive and collaborative web, where user is at the center of service in terms of publications and reactions. This transforms the user from his old status as a consumer to a new one as a producer. Folksonomies are one of the technologies of Web 2.0 that permit users to annotate resources on the Web. This is done by allowing users to use any keyword or tag that they find relevant. Although folksonomies require a context-independent and inter-subjective definition of meaning, many researchers have proven the existence of an implicit semantics in these unstructured data. In this paper, we propose an improvement of our previous approach to extract ontological structures from folksonomies. The major contributions of this paper are a Normalized Co-occurrences in Distinct Users (NCDU) similarity measure, and a new algorithm to define context of tags and detect ambiguous ones. We compared our similarity measure to a widely used method for identifying similar tags based on the cosine measure. We also compared the new algorithm with the Fuzzy Clustering Algorithm (FCM) used in our original approach. The evaluation shows promising results and emphasizes the advantage of our approach.
... @BULLET Clustering approaches: These approaches identify the semantics of tags by clustering tags according to some relations among them (Mika, 2007; Hamasaki, 2007; Begelman, 2006; Kennedy, 2007; Heymann, 2006; Benz, 2010; Marouf, 2013). These clustering approaches measure tags similarity based only on one dimension (user or resource), and most of them do not deal with ambiguity problem or do not give a formal solution to it. ...
... In this section, we describe an extension to our approach that aims to extract ontological structures from folksonomies (Marouf, 2013). The new approach has the same steps as the old one, but it overcomes some limitations that are discussed one by one in the following sections. ...
... Inspired by prior work (Marouf, 2013), and after having limited success-producing clusters with other algorithms, we developed algorithm 2, a new fuzzy clustering algorithm to group similar tags into clusters and to identify ambiguous tags. The algorithm starts by adding the first tag in the generality list as the first center (line 1), and initializing the number of clusters " c " (line 2). ...
Folksonomies are one of the technologies of Web 2.0 that permit users to annotate resources on the Web. In this paper, the authors propose an integrated approach to extract ontological structures from unstructured and semi-structured resources. Our proposal overcome limitations of existing approaches. It gives a formal, simple, and efficient solution to the tag clustering and disambiguation problem. Moreover, their approach doesn't need any ontology as an upper guide during the generation process. The generated ontology can be used to enhance various tasks such as ontology evolution and enrichment.
