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Enrico Motta - One of the best experts on this subject based on the ideXlab platform.
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Improving Folksonomies using formal knowledge: A case study on search
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009Co-Authors: Sofia Angeletou, Marta Sabou, Enrico MottaAbstract:Search in Folksonomies is impeded by lack of machine understandable descriptions for the meaning of tags and their relations. One approach to addressing this problem is the use of formal knowledge resources (KS) to assign meaning to the tags, most notably WordNet and (online) ontologies. However, there is no insight of how the different characteristics of such KS can contribute to improving search in Folksonomies. In this work we compare the two KS in the context of folksonomy search, first by evaluating the enriched structures and then by performing a user study on searching the folksonomy content through these structures. We also compare them to cluster-based folksonomy search. We show that the diversity of ontologies leads to more satisfactory results compared to WordNet although the latter provides richer structures. We also conclude that the idiosyncrasies of Folksonomies can not be addressed by only using formal KS.
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Semantically enriching Folksonomies with FLOR
CEUR Workshop Proceedings, 2008Co-Authors: Sofia Angeletou, Marta Sabou, Enrico MottaAbstract:While the increasing popularity of Folksonomies has lead to a vast quantity of tagged data, resource retrieval in these systems is limited by them being agnostic to the meaning (i.e., semantics) of tags. Our goal is to automatically enrich folksonomy tags (and implicitly the related resources) with formal semantics by associating them to relevant concepts defined in online ontologies. We introduce FLOR, a mechanism for automatic folksonomy enrichment by combining knowledge from WordNet and online ontologies.We experimentally tested FLOR on tag sets drawn from 226 Flickr photos and obtained a precision value of 93% and an approximate recall of 49%.
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integrating Folksonomies with the semantic web
European Semantic Web Conference, 2007Co-Authors: Lucia Specia, Enrico MottaAbstract:While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.
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Bridging the gap between Folksonomies and the semantic web: an experience report
ESWC, 2007Co-Authors: Sofia Angeletou, Marta Sabou, Lucia Specia, Enrico MottaAbstract:While Folksonomies allow tagging of similar resources with a variety of tags, their content retrieval mechanisms are severely ham- pered 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 se- lecting 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 Seman- tic Web, it is beneficial for both.
Michel Buffa - One of the best experts on this subject based on the ideXlab platform.
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SemTagP: Semantic community detection in Folksonomies
Proceedings - 2011 IEEE WIC ACM International Conference on Web Intelligence WI 2011, 2011Co-Authors: Guillaume Erétéo, Fabien Gandon, Michel BuffaAbstract:Building on top of our results on semantic social network analysis, we present a community detection algorithm, SemTagP, that takes benefits of the semantic data that were captured while structuring the RDF graphs of social networks. SemTagP not only offers to detect but also to label communities by exploiting (in addition to the structure of the social graph) the tags used by people during the social tagging process as well as the semantic relations inferred between tags. Doing so, we are able to refine the partitioning of the social graph with semantic processing and to label the activity of detected communities. We tested and evaluated this algorithm on the social network built from Ph.D. theses funded by ADEME, the French Environment and Energy Management Agency. We showed how this approach allows us to detect and label communities of interest and control the precision of the labels.
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helping online communities to semantically enrich Folksonomies
Web Science, 2010Co-Authors: Freddy Limpens, Fabien Gandon, Michel BuffaAbstract:This paper presents our approach to collaborative and semi- automated semantic structuring of Folksonomies. Tags freely provided by users of online communities are not semanti- cally linked, and this hinders significantly the potentials for browsing and exploring these data. We propose a socio- technical system combining automatic handlings of tags, us- ing state of the art algorithm, and user friendly interfaces designed after a careful analysis of the usage of our target communities. Much like Folksonomies, our socio-technical system lets each user maintain his own view while still ben- efiting from others contributions. As a complement to sim- ilar approaches, our approach supports conflicting point of views all along the life-cycle of semantically enriched folk- sonomies.
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Bridging ontologies and Folksonomies to leverage knowledge sharing on the social web: A brief survey
Aramis 2008 - 1st International Workshop on Automated engineeRing of Autonomous and runtiMe evolvIng Systems, and ASE2008 the 23rd IEEE/ACM Int. Conf., 2008Co-Authors: Freddy Limpens, Fabien Gandon, Michel BuffaAbstract:Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the Folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and Folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of Folksonomies with formal languages and ontologies from the Semantic Web.
Gerd Stumme - One of the best experts on this subject based on the ideXlab platform.
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evaluating similarity measures for emergent semantics of social tagging
The Web Conference, 2009Co-Authors: Benjamin Markines, Andreas Hotho, Ciro Cattuto, Filippo Menczer, Dominik Benz, Gerd StummeAbstract:Social bookmarking systems are becoming increasingly important data sources for bootstrapping and maintaining Semantic Web applications. Their emergent information structures have become known as Folksonomies. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to Folksonomies, and which measures are best suited for applications such as community detection, navigation support, semantic search, user profiling and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures, which are derived from several established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity between tags and between resources and consider different methods to aggregate annotations across users. After comparing the ability of several tag similarity measures to predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory Project. We also investigate the issue of scalability. We find that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.
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formal concept analysis
Handbook on Ontologies, 2009Co-Authors: Gerd StummeAbstract:Formal concept analysis (FCA) is a mathematical theory about concepts and concept hierarchies. Based on lattice theory, it allows to derive concept hierarchies from datasets. In this survey, we recall the basic notions of FCA, including its relationship to Folksonomies. The survey is concluded by a list of FCA based knowledge engineering solutions.
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discovering shared conceptualizations in Folksonomies
Social Science Research Network, 2008Co-Authors: Robert Jäschke, Andreas Hotho, Christoph Schmitz, Bernhard Ganter, Gerd StummeAbstract:Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called Folksonomies. Unlike ontologies, shared conceptualisations are not formalised, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualisations. Our approach extends the data mining task of discovering all closed itemsets to three- dimensional data structures to allow for mining Folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.
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network properties of Folksonomies
Ai Communications, 2007Co-Authors: Ciro Cattuto Aff N, Andrea Baldassarri, Andreas Hotho, Christoph Schmitz, Vito Servedio Aff D P N, Vittorio Loreto Aff N, Miranda Grahl, Gerd StummeAbstract:Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures - so-called Folksonomies - as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.
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tag recommendations in Folksonomies
LWA, 2007Co-Authors: Robert Jäschke, Andreas Hotho, Leandro Balby Marinho, Lars Schmidtthieme, Gerd StummeAbstract:Collaborative tagging systems allow users to assign keywords--so called "tags"--to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.
Yedid Nadina - One of the best experts on this subject based on the ideXlab platform.
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Análisis práctico de folksonomías: el caso de los gestores bibliográficos sociales
Instituto de Investigaciones Bibliotecológicas INIBI. Facultad de Filosofía y Letras Universidad de Buenos Aires, 2016Co-Authors: Yedid NadinaAbstract:Practical analysis of Folksonomies: the case of the reference management software. An analysis of the Folksonomies developed in reference management software is performed, as a way to identify their potential use for the information retrieval in low controlled digital environments. There is also a proposal to identify methods and techniques not currently used in those websites, to improve the quality of the Folksonomies as information retrieval tools. For that reason, the Folksonomies developed at the reference management software CiteULike, Mendeley and Bibsonomy are studied. Their principal characteristics regarding type, form and construction of the tags are detailed. The interfaces used for creation and edition of the tags in each one of the reference management software are also described. The conclusion is that, based on the form and structure used to create them, the Folksonomies might be useful for information retrieval at this kind of website, and new methods and techniques are proposed to improve the quality of the folksonomy tags
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Análisis práctico de folksonomías: el caso de los gestores bibliográficos sociales
'Editorial de la Facultad de Filosofia y Letras - Universidad de Buenos Aires', 2016Co-Authors: Yedid NadinaAbstract:An analysis of the Folksonomies developed in reference management software is performed, as a way to identify their potential use for the information retrieval in low controlled digital environments. There is also a proposal to identify methods and techniques not currently used in those websites, to improve the quality of the Folksonomies as information retrieval tools. For that reason, the Folksonomies developed at the reference management software CiteULike, Mendeley and Bibsonomy are studied. Their principal characteristics regarding type, form and construction of the tags are detailed. The interfaces used for creation and edition of the tags in each one of the reference management software are also described. The conclusion is that, based on the form and structure used to create them, the Folksonomies might be useful for information retrieval at this kind of website, and new methods and techniques are proposed to improve the quality of the folksonomy tags.Se presenta un análisis de las folksonomías desarrolladas en los gestores bibliográficos sociales, como medio para identificar el potencial uso de las mismas en la recuperación de información en entornos digitales poco controlados. Se propone también identificar métodos y técnicas no contempladas actualmente en esos sitios web, para mejorar la calidad de las folksonomías en cuanto herramientas para la recuperación de la información. A tales fines, se estudian las folksonomías desarrolladas en los gestores bibliográficos CiteULike, Mendeley y Bibsonomy. Se detallan sus principales características en cuanto a tipo, forma y construcción de las etiquetas. Se describen también las interfaces de creación y edición de etiquetas en cada uno de estos gestores. Se concluye que las folksonomías pueden resultar útiles para la recuperación de información en los gestores bibliográficos, en función de la forma y estructura con la que son creadas, y se proponen nuevos métodos y técnicas para mejorar la calidad de las mismas
Lucia Specia - One of the best experts on this subject based on the ideXlab platform.
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integrating Folksonomies with the semantic web
European Semantic Web Conference, 2007Co-Authors: Lucia Specia, Enrico MottaAbstract:While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.
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Bridging the gap between Folksonomies and the semantic web: an experience report
ESWC, 2007Co-Authors: Sofia Angeletou, Marta Sabou, Lucia Specia, Enrico MottaAbstract:While Folksonomies allow tagging of similar resources with a variety of tags, their content retrieval mechanisms are severely ham- pered 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 se- lecting 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 Seman- tic Web, it is beneficial for both.