Understanding the Semantics of Ambiguous Tags in Folksonomies

The use of tags to describe Web resources in a collaborative manner has experienced rising popularity among Web users in recent years. The product of such activity is given the name folksonomy, which can be considered as a scheme of organizing information in the users' own way. In this paper, we present a possible way to analyze the tripartite graphs - graphs involving users, tags and resources - of folksonomies and discuss how these elements acquire their meanings through their associations with other elements, a process we call mutual contextualization. In particular, we demonstrate how different meanings of ambiguous tags can be discovered through such analysis of the tripartite graph by studying the tag sf. We also discuss how the result can be used as a basis to better understand the nature of folksonomies

An Algorithm for Detecting Communities in Folksonomy Hypergraphs

International audienceIn this article, we are interested in social resource sharing systems such as Flickr, which use a lightweight knowledge representation called folksonomy. One of the fundamental questions asked by sociologists and actors involved in these online communities is to know whether a coherent tags categorization scheme emerges at global scale from folksonomy, though the users don’t share the same vocabulary. In order to satisfy their needs, we propose an algorithm to detect clusters in folksonomies hypergraphs by generalizing the Girvan and Newman’s clustering algorithm. We test our algorithm on a sample of an hypergragh of tag co-occurrence extracted from Flickr in September 2006, which gives promising results

SEMANTIC SOCIAL NETWORK ANALYSIS FOR THE ENTERPRISE

Business processes are generally fixed and enforced strictly, as reflected by the static nature of underlying software systems and datasets. However, internal and external situations, organizational changes and various other factors trigger dynamism, which is reflected in the form of issues, complains, Q&A, opinions, reviews, etc, over a plethora of communication channels, such as email, chat, discussion forums, and internal social network. Careful and timely analysis and processing of such channels may lead to early detection of emerging trends, critical issues, opportunities, topics of interests, contributors, experts etc. Social network analytics have been successfully applied in general purpose, online social network platforms, like Facebook and Twitter. However, in order for such techniques to be useful in business context, it is mandatory to integrate them with underlying business systems, processes and practices. Such integration problem is increasingly recognized as Big Data problem. We argue that SemanticWeb technology applied with social network analytics can solve enterprise knowledge management, while achieving integration

Social and Semantic Contexts in Tourist Mobile Applications

The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

Performance Tags- Who's running the show?

We describe a pilot study which specifically examines the prevalence and characteristics of performance tags on several sites. Identifying post-coordination of tags as a useful step in the study of this phenomenon, as well as other approaches to leveraging tags based on text and/or sentiment analysis, we demonstrate an approach to automation of this process, postcoordinating (segmenting) terms by means of a probabilistic model based around Markov chains. The effectiveness of this approach to parsing is evaluated with respect to the wide range of constructions visible on various services. Several candidate approaches for the latter stages of automated classification are identified

Identificação de critérios para avaliação de ideias: um método utilizando folksonomias

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2016.As ferramentas de cocriação encontram uma rica fonte de conhecimento baseada nas interações sociais que ocorrem na Web. Essa interação coletiva é a principal característica dos Sistemas de apoio à inovação, em especial para os sistemas de gestão de ideias. Entretanto, para avaliar ideias, as soluções atuais limitam-se a métodos baseados em formulários com critérios pré-estabelecidos ou, então, por métricas de engajamento social. O contexto organizacional é crítico para o sucesso de uma ideia, porém, ao considerar apenas índices de popularidade, as avaliações não agregam semanticamente o conhecimento atribuído pelo usuário, bem como não determinam quais critérios foram ponderados pela comunidade. A fim de compreender este conhecimento coletivo, a presente pesquisa propõe um método de identificação e análise de critérios para a avaliação de ideias. O desenvolvimento desse artefato é baseado na metodologia da ciência do design e explora o conhecimento a partir de atribuições sociais por notas e tags, as folksonomias. Assim, no contexto do front end da Inovação, o método representa uma apropriação semântica e qualitativa dos critérios atribuídos pela comunidade. A verificação utiliza técnicas da mineração de folksonomias em uma base de dados representada por um modelo de hipergrafo. Como resultado, o método permite evidenciar um conjunto de características a serem consideradas pela organização como critérios de avaliação. Além disso, a solução constata que a popularidade não é uma medida de consenso da comunidade, portanto sub comunidades auferem medidas mais precisas em suas atribuições; e a flexibilização temporal, própria das interações sociais, colaboram na recomendação de ideias baseada em tendências e no contexto organizacional.Abstract : Co-creation tools meet a rich source of knowledge on social interactions that occurs on the Web. This collective interaction is the main characteristic of innovation support systems, especially idea management systems. However, in order to evaluate ideas, current solutions are limited to methods based on forms with pre-established criteria or metrics of social engagement. The organizational context is critical to the success of an idea. Nevertheless, when considering just popularity ratings, the evaluations do not semantically aggregate the knowledge attributed by the user. It also does not determine what criteria was weighted by the community. In order to understand this collective knowledge, the present research proposes a method for identification and analysis of criteria in idea evaluation. The development of this artefact is based on the design science research methodology, and it explores the knowledge from social attributions using grades and tags, also known as folksonomy. Therefore, within the front end of innovation, the method represents a semantic, qualitative appropriation of criteria attributed by the community. The artefact was verified using folksonomy mining techniques in a database represented by a hypergraph model. As a result, the method allows to visualize a set of characteristics to be considered as evaluation criteria by any organization. In addition, the results showed that popularity is not a community s consensus measure. Therefore, sub communities get more precise measurements in their attributes; and temporal flexibility, which is specific to social interactions, collaborate on the idea recommendation based on trends and organizational context

Spatiotemporal user and place modelling on the geo-social web

Users of Location-Based Social Networks (LBSN) are giving away information about their whereabouts, and their interactions in the geographic space. In comparison to other types of personal data, location data are sensitive and can reveal user’s daily routines, activities, experiences and interests in the physical world. As a result, the user is facing an information overload that overburdens him to make a satisfied decision on where to go or what to do in a place. Thus, finding the matching places, users and content is one of the key challenges in LSBNs. This thesis investigates the different dimensions of data collected on LBSNs and proposes a user and place modelling framework. In particular, this thesis proposes a novel approach for the construction of different views of personal user profiles that reflect their interest in geographic places, and how they interact with geographic places. Three novel modelling frameworks are proposed, the static user model, the dynamic user model and the semantic place model. The static user model is a basic model that is used to represent the overall user interactions towards places. On the other hand, the dynamic user model captures the change of the user’s preferences over time. The semantic place model identifies user activities in places and models the relationships between places, users, implicit place types, and implicit activities. The proposed models demonstrate how geographic place characteristics as well as implicit user interactions in the physical space can further enrich the user profiles. The enrichment method proposed is a novel method that combines the semantic and the spatial influences into user profiles. Evaluation of the proposed methods is carried out using realistic data sets collected from the Foursquare LBSN. A new Location and content recommendation methods are designed and implemented to enhance existing location recommendation methods and results showed the usefulness of considering place semantics and the time dimension when the proposed user profiles in recommending locations and content. The thesis considers two further related problems; namely, the construction of dynamic place profiles and computing the similarity between users on LBSN. Dynamic place profiles are representations of geographic places through users’ interaction with the places. In comparison to static place models represented in gazetteers and map databases, these place profiles provide a dynamic view of how the places are used by actual people visiting and interacting with places on the LBSN. The different views of personal user profiles constructed within our framework are used for computing the similarity between users on the LBSN. Temporal user similarities on both the semantic and spatial levels are proposed and evaluated. Results of this work show the challenges and potential of the user data collected on LBSN

Addressing the cold start problem in tag-based recommender systems

Folksonomies have become a powerful tool to describe, discover, search, and navigate online resources (e.g., pictures, videos, blogs) on the Social Web. Unlike taxonomies and ontologies, which impose a hierarchical categorisation on content, folksonomies directly allow end users to freely create and choose the categories (in this case, tags) that best describe a piece of information. However, the freedom aafforded to users comes at a cost: as tags are defined informally, the retrieval of information becomes more challenging. Different solutions have been proposed to help users discover content in this highly dynamic setting. However, they have proved to be effective only for users who have already heavily used the system (active users) and who are interested in popular items (i.e., items tagged by many other users). In this thesis we explore principles to help both active users and more importantly new or inactive users (cold starters) to find content they are interested in even when this content falls into the long tail of medium-to-low popularity items (cold start items). We investigate the tagging behaviour of users on content and show how the similarities between users and tags can be used to produce better recommendations. We then analyse how users create new content on social tagging websites and show how preferences of only a small portion of active users (leaders), responsible for the vast majority of the tagged content, can be used to improve the recommender system's scalability. We also investigate the growth of the number of users, items and tags in the system over time. We then show how this information can be used to decide whether the benefits of an update of the data structures modelling the system outweigh the corresponding cost. In this work we formalize the ideas introduced above and we describe their implementation. To demonstrate the improvements of our proposal in recommendation efficacy and efficiency, we report the results of an extensive evaluation conducted on three different social tagging websites: CiteULike, Bibsonomy and MovieLens. Our results demonstrate that our approach achieves higher accuracy than state-of-the-art systems for cold start users and for users searching for cold start items. Moreover, while accuracy of our technique is comparable to other techniques for active users, the computational cost that it requires is much smaller. In other words our approach is more scalable and thus more suitable for large and quickly growing settings

Leveraging social relevance : using social networks to enhance literature access and microblog search

L'objectif principal d'un système de recherche d'information est de sélectionner les documents pertinents qui répondent au besoin en information exprimé par l'utilisateur à travers une requête. Depuis les années 1970-1980, divers modèles théoriques ont été proposés dans ce sens pour représenter les documents et les requêtes d'une part et les apparier d'autre part, indépendamment de tout utilisateur. Plus récemment, l'arrivée du Web 2.0 ou le Web social a remis en cause l'efficacité de ces modèles du fait qu'ils ignorent l'environnement dans lequel l'information se situe. En effet, l'utilisateur n'est plus un simple consommateur de l'information mais il participe également à sa production. Pour accélérer la production de l'information et améliorer la qualité de son travail, l'utilisateur échange de l'information avec son voisinage social dont il partage les mêmes centres d'intérêt. Il préfère généralement obtenir l'information d'un contact direct plutôt qu'à partir d'une source anonyme. Ainsi, l'utilisateur, influencé par son environnement socio-cultuel, donne autant d'importance à la proximité sociale de la ressource d'information autant qu'à la similarité des documents à sa requête. Dans le but de répondre à ces nouvelles attentes, la recherche d'information s'oriente vers l'implication de l'utilisateur et de sa composante sociale dans le processus de la recherche. Ainsi, le nouvel enjeu de la recherche d'information est de modéliser la pertinence compte tenu de la position sociale et de l'influence de sa communauté. Le second enjeu est d'apprendre à produire un ordre de pertinence qui traduise le mieux possible l'importance et l'autorité sociale. C'est dans ce cadre précis, que s'inscrit notre travail. Notre objectif est d'estimer une pertinence sociale en intégrant d'une part les caractéristiques sociales des ressources et d'autre part les mesures de pertinence basées sur les principes de la recherche d'information classique. Nous proposons dans cette thèse d'intégrer le réseau social d'information dans le processus de recherche d'information afin d'utiliser les relations sociales entre les acteurs sociaux comme une source d'évidence pour mesurer la pertinence d'un document en réponse à une requête. Deux modèles de recherche d'information sociale ont été proposés à des cadres applicatifs différents : la recherche d'information bibliographique et la recherche d'information dans les microblogs. Les importantes contributions de chaque modèle sont détaillées dans la suite. Un modèle social pour la recherche d'information bibliographique. Nous avons proposé un modèle générique de la recherche d'information sociale, déployé particulièrement pour l'accès aux ressources bibliographiques. Ce modèle représente les publications scientifiques au sein d'réseau social et évalue leur importance selon la position des auteurs dans le réseau. Comparativement aux approches précédentes, ce modèle intègre des nouvelles entités sociales représentées par les annotateurs et les annotations sociales. En plus des liens de coauteur, ce modèle exploite deux autres types de relations sociales : la citation et l'annotation sociale. Enfin, nous proposons de pondérer ces relations en tenant compte de la position des auteurs dans le réseau social et de leurs mutuelles collaborations. Un modèle social pour la recherche d'information dans les microblogs.} Nous avons proposé un modèle pour la recherche de tweets qui évalue la qualité des tweets selon deux contextes: le contexte social et le contexte temporel. Considérant cela, la qualité d'un tweet est estimé par l'importance sociale du blogueur correspondant. L'importance du blogueur est calculée par l'application de l'algorithme PageRank sur le réseau d'influence sociale. Dans ce même objectif, la qualité d'un tweet est évaluée selon sa date de publication. Les tweets soumis dans les périodes d'activité d'un terme de la requête sont alors caractérisés par une plus grande importance. Enfin, nous proposons d'intégrer l'importance sociale du blogueur et la magnitude temporelle avec les autres facteurs de pertinence en utilisant un modèle Bayésien.An information retrieval system aims at selecting relevant documents that meet user's information needs expressed with a textual query. For the years 1970-1980, various theoretical models have been proposed in this direction to represent, on the one hand, documents and queries and on the other hand to match information needs independently of the user. More recently, the arrival of Web 2.0, known also as the social Web, has questioned the effectiveness of these models since they ignore the environment in which the information is located. In fact, the user is no longer a simple consumer of information but also involved in its production. To accelerate the production of information and improve the quality of their work, users tend to exchange documents with their social neighborhood that shares the same interests. It is commonly preferred to obtain information from a direct contact rather than from an anonymous source. Thus, the user, under the influenced of his social environment, gives as much importance to the social prominence of the information as the textual similarity of documents at the query. In order to meet these new prospects, information retrieval is moving towards novel user centric approaches that take into account the social context within the retrieval process. Thus, the new challenge of an information retrieval system is to model the relevance with regards to the social position and the influence of individuals in their community. The second challenge is produce an accurate ranking of relevance that reflects as closely as possible the importance and the social authority of information producers. It is in this specific context that fits our work. Our goal is to estimate the social relevance of documents by integrating the social characteristics of resources as well as relevance metrics as defined in classical information retrieval field. We propose in this work to integrate the social information network in the retrieval process and exploit the social relations between social actors as a source of evidence to measure the relevance of a document in response to a query. Two social information retrieval models have been proposed in different application frameworks: literature access and microblog retrieval. The main contributions of each model are detailed in the following. A social information model for flexible literature access. We proposed a generic social information retrieval model for literature access. This model represents scientific papers within a social network and evaluates their importance according to the position of respective authors in the network. Compared to previous approaches, this model incorporates new social entities represented by annotators and social annotations (tags). In addition to co-authorships, this model includes two other types of social relationships: citation and social annotation. Finally, we propose to weight these relationships according to the position of authors in the social network and their mutual collaborations. A social model for information retrieval for microblog search. We proposed a microblog retrieval model that evaluates the quality of tweets in two contexts: the social context and temporal context. The quality of a tweet is estimated by the social importance of the corresponding blogger. In particular, blogger's importance is calculated by the applying PageRank algorithm on the network of social influence. With the same aim, the quality of a tweet is evaluated according to its date of publication. Tweets submitted in periods of activity of query terms are then characterized by a greater importance. Finally, we propose to integrate the social importance of blogger and the temporal magnitude tweets as well as other relevance factors using a Bayesian network model