Emergent Semantics from Folksonomies: A Quantitative Study (original) (raw)
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Semantic Emergence From Social Tagging Systems
International Journal of Organizational and Collective Intelligence, 2015
Recently, Social tagging systems (folksonomies) have become very popular platforms where content is created collaboratively by users. This kind of environments allows users to assign shared resources with freely chosen keywords (tags). Folksonomies provide a valuable addition to the knowledge organization methods since they allow users to choose vocabularies that meet their real tastes and cognition. However, the lacking of standardization and the flat structure of tags in folksonomies pose challenges for folksonomy searching and information retrieval. Several researches have been proposed to overcome these drawbacks. In this paper, the authors present, describe and compare the most relevant approaches to capturing hidden semantics in folksonomies and turning it into ontologies. The authors also present and describe many techniques, tools and online resources that can be useful in working on such systems. Finally, the authors propose an approach to extract ontology from social taggi...
Information Systems, 2009
In this paper we present a new approach to supporting users to annotate and browse resources referred by a folksonomy. Our approach is characterized by the following novelties: (i) it proposes a probabilistic technique to quickly and accurately determine the similarity and the generalization degrees of two tags; (ii) it proposes two hierarchical structures and two related algorithms to arrange groups of semantically related tags in a hierarchy; this allows users to visualize tags of their interests according to desired semantic granularities and, then, helps them to find those tags best expressing their information needs. In this paper we first illustrate the technical characteristics of our approach; then we describe various experiments allowing its performance to be tested; finally, we compare it with other related approaches already proposed in the literature.
An Integrated Approach to Drive Ontological Structure from Folksonomie
International Journal of Information Technology and Computer Science, 2014
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.
Fuzzzy.com, a social bookmarking website has been developed to study collaborative creation of semantics. In a shared online space, users of Fuzzzy continuously create metadata bottom-up by categorizing (tagging) favourite hyperlinks (bookmarks). The semantic network of tags created by users evolves into a people's fuzzy common ontology ("folktology"). We discuss several social and cognitive aspects of Topic Maps technology and scalability by analyzing the use of the system. We further argue that holistic knowledge creation and interchange is highly needed. Our results from Fuzzzy suggest that this can be realized by connecting distributed knowledge centric communities of dedicated users within specific domains.
A Complete Life-Cycle for the Semantic Enrichment of Folksonomies
Studies in Computational Intelligence, 2013
Tags freely provided by users of social tagging services are not explicitly semantically linked, and this significantly hinders the possibilities for browsing and exploring these data. On the other hand, folksonomies provide great opportunities to bootstrap the construction of thesauri. We propose an approach to semantic enrichment of folksonomies that integrates both automatic processing and user input, while formally supporting multiple points of view. We take into account the social structure of our target communities to integrate the folksonomy enrichment process into everyday tasks. Our system allows individual users to navigate more efficiently within folksonomies, and also to maintain their own structure of tags while benefiting from others contributions. Our approach brings also solutions to the bottleneck problem of knowledge acquisition by helping communities to build thesauri by integrating the manifold contributions of all their members, thus providing for a truly socio-semantic solution to folksonomy enrichment and thesauri construction.
Ontology of Folksonomy: A New Modeling Method
Ontologies and tagging systems are two different ways to organize the knowledge present in Web. The first one has a formal fundamental that derives from descriptive logic and artificial intelligence. The other one is simpler and it integrates heterogeneous contents, and it is based on the collaboration of users in the Web 2.0. In this paper we propose a method to model tagging systems like folksonomies using ontologies. In our proposal, structured information (ontologies) can be extracted from knowledge built in a simple and collaborative way (folksonomies). Furthermore, we provide an analytical expression to evaluate the system requirements to store the derived ontology.
Modeling Ontology of Folksonomy with Latent Semantics of Tags
Abstract Modeling ontology of folksonomy provides a way of learning light weight ontology's which is a hot topic investigated recently. Previous approaches for modeling ontology of folksonomy either ignores semantics (synonymy, hyponymy or polysemy) or do not simultaneously consider relationships between actors (users), concepts (tags) and instances (resources) or are based on the idea that title words are responsible for generating tags for resources.
Ontology of Folksonomy: A New Modelling Method
2007
Ontologies and tagging systems are two different ways to organize the knowledge present in Web. The first one has a formal fundamental that derives from descriptive logic and artificial intelligence. The other one is simpler and it integrates heterogeneous contents, and it is based on the collaboration of users in the Web 2.0. In this paper we propose a method to model tagging systems like folksonomies using ontologies. In our proposal, structured information (ontologies) can be extracted from knowledge built in a simple and collaborative way (folksonomies). Furthermore, we provide an analytical expression to evaluate the system requirements to store the derived ontology.
Bridging the Gap Between Folksonomies and the Semantic Web: An Experience Report
. Bridging the gap between folksonomies and the semantic web: an experience report. Abstract. While folksonomies allow tagging of similar resources with a variety of tags, their content retrieval mechanisms are severely hampered by being agnostic to the relations that exist between these tags. To overcome this limitation, several methods have been proposed to find groups of implicitly inter-related tags. We believe that content retrieval can be further improved by making the relations between tags explicit. In this paper we propose the semantic enrichment of folksonomy tags with explicit relations by harvesting the Semantic Web, i.e., dynamically selecting and combining relevant bits of knowledge from online ontologies. Our experimental results show that, while semantic enrichment needs to be aware of the particular characteristics of folksonomies and the Semantic Web, it is beneficial for both.
Social semantic cloud of tags: semantic model for folksonomies
Knowledge Management Research & Practice, 2010
A growing number of tagging applications have begun to provide users the ability to socialise their own keywords. Tagging, which assigns a set of keywords to resources, has become a powerful way for organising, browsing, and publicly sharing personal collections of resources on the Web. It is called folksonomies. These systems on current social websites, however, have deficiencies in defining tag's meaning, and are often blocked to users in order to reuse, share, and exchange the tags across heterogeneous websites. In this paper, we describe a semantic model for expressing folksonomies in social websites. This model, called Social Semantic Cloud of Tags, aims to provide a consistent format of representing folksonomies and some features in terms of tagging activities. We describe core concepts and relevant properties such as a popularity and usage of tags, along with deduced relationships between tags. We will discuss how this model helps to reduce drawbacks regarding tag sharing between users, applications, or folksonomies.