Constellations: A participatory, online application for research collaboration in higher education interdisciplinary courses

The research establishes a model for online learning centering on the needs of integrative knowledge practices. Through the metaphor of Constellations, the practice-based research explores the complexities of working within interdisciplinary learning contexts and the potential of tools such as the Folksonomy learning platform for providing necessary conceptual support.

Sistema para indexação e visualização de depoimentos de história oral: o caso do Museu da Pessoa

Esta dissertação apresenta a estruturação de um sistema para indexação e visualização de depoimentos de história oral em vídeo. A partir do levantamento de um referencial teórico referente à indexação, o sistema resultou em um protótipo funcional de alta fidelidade. O conteúdo para a realização deste foi obtido pela indexação de 12 depoimentos coletados pela equipe do Museu da Pessoa durante o projeto Memórias da Vila Madalena, em São Paulo (ago/2012). Acervos de História Oral como o Museu da Pessoa, o Museu da Imagem e do Som ou o Centro de Pesquisa e Documentação de História Contemporânea do Brasil / CPDOC da Fundação Getúlio Vargas, reúnem milhares de horas de depoimentos em áudio e vídeo. De uma forma geral, esses depoimentos são longas entrevistas individuais, onde diversos assuntos são abordados; o que dificulta sua análise, síntese e consequentemente, sua recuperação. A transcrição dos depoimentos permite a realização de buscas textuais para acessar assuntos específicos nas longas entrevistas. Por isso, podemos dizer que as transcrições são a principal fonte de consulta dos pesquisadores de história oral, deixando a fonte primária (o vídeo) para um eventual segundo momento da pesquisa. A presente proposta visa ampliar a recuperação das fontes primárias a partir da indexação de segmentos de vídeo, criando pontos de acesso imediato para trechos relevantes das entrevistas. Nessa abordagem, os indexadores (termos, tags ou anotações) não são associados ao vídeo completo, mas a pontos de entrada e saída (timecodes) que definem trechos específicos no vídeo. As tags combinadas com os timecodes criam novos desafios e possibilidades para indexação e navegação através de arquivos de vídeo. O sistema aqui estruturado integra conceitos e técnicas de áreas aparentemente desconectadas: metodologias de indexação, construção de taxonomias, folksonomias, visualização de dados e design de interação são integrados em um processo unificado que vai desde a coleta e indexação dos depoimentos até sua visualização e interação.

协同过滤推荐系统的标签网络整合及增量化

随着信息过载问题越来越突出，如何有获地提取互联网上的信息成为近年来的一个研究热点。个性化推荐系统（Personalized Recommender System）利用用户的兴趣为其推荐最相关的互联网信息，已成功部署于搜索引擎、电子商务、网上社区等互联网关键应用中，是信息检索界的一个突破性的领域。个性化推荐系统的广泛商业应用对其性能提出了严格的要求，而数据的稀疏性和海量性 大大限制了推荐的质量。为了获得更高的准确性和可扩展性，协同过滤方法的 成功应用提供了一条解决之路。协同过滤的思想是利用兴趣相投、经验相似的 群体的喜好来为其内部成员推荐感兴趣的信息，用户通过如评分等的机制表现 自己的偏好以达到为自己和他人过滤信息的目的。作为目前最成功的推荐方法， 协同过滤的应用已经比较成熟。然而这种推荐系统仍有很大改进余地。 标签网络（Folksonomy）是一种最近兴起的社会网络资源，用户通过对浏览 过的物品进行注释或给予标签达到对其归类的作用。像这样对同一物品集合的给予标签的行为就形成这被称为标签网络的社会网络。协同过滤的思想无疑可以用于这些数据，为这些用户对这一物品集合内的元素给予推荐。将标签网络数据融入原基于评分的推荐系统，是我们的主要贡献之一。我们提出了两种具体地使用标签网络数据辅助评分预测的方法。一种是友邻方法，直接利用聚类方法寻找相似的用户或物品；另一种是联合矩阵分解，利用机器学习领域的矩阵分解拟合未知元素的方法预测评分。这两种方法的想法的初衷均来源于协同过滤技术面对的数据的一个棘手的特性，数据稀疏性。伴随着协同过滤的发展还有另外一个问题，那就是数量的巨大维度。对这一问题，我们提出了一种增量化方法使推荐系统适应目益增长的数据量。协同过滤方法中有一大类是利用聚类算法做出推荐，我们所提出的改进细化了聚类算法的粒度，使每次聚类都是有针对性地对小容量的集合进行操作。我们将改进的矩阵分解方法应用于每个集合的聚类操作上，使得相似的用户和物品之间的关系更加紧密。这在数据更新率很高的情况下可以避免不断重新将整个数据集进行训练的问题。我们通过实验对比了流行的若干种推荐算法，证明了我们所提出的方法均有着比较大的性能提升。不仅仅拥有更高的准确性，而且也拥有非常好的可扩展性（即算法时间复杂度与数据规模线性相关）。

Analyse documentaire en milieu universitaire : deux approches générales comparées

Ce mémoire porte sur l’analyse documentaire en milieu universitaire. Deux approches générales sont d’abord étudiées : l’approche centrée sur le document (premier chapitre), prédominante dans la tradition bibliothéconomique, et l’approche centrée sur l’usager (deuxième chapitre), influencée par le développement d’outils le plus souvent associés au Web 2.0. L’opposition entre ces deux démarches reflète une dichotomie qui se trouve au cœur de la notion de sujet, c’est-à-dire les dimensions objective et subjective du sujet. Ce mémoire prend par conséquent la forme d’une dissertation dont l’avantage principal est de considérer à la fois d’importants acquis qui appartiennent à la tradition bibliothéconomique, à la fois des développements plus récents ayant un impact important sur l’évolution de l’analyse documentaire en milieu universitaire. Notre hypothèse est que ces deux tendances générales doivent être mises en relief afin d’approfondir la problématique de l’appariement, laquelle définit la difficulté d’accorder le vocabulaire qu’utilise l’usager dans ses recherches documentaires avec celui issu de l’analyse documentaire (métadonnées sujet). Dans le troisième chapitre, nous examinons certaines particularités liées à l’utilisation de la documentation en milieu universitaire dans le but de repérer certaines possibilités et certaines exigences de l’analyse documentaire dans un tel milieu. À partir d’éléments basés sur l’analyse des domaines d’études et sur la démarche analytico-synthétique, il s’agit d’accentuer l’interaction potentielle entre usagers et analystes documentaires sur le plan du vocabulaire utilisé de part et d’autre.

Small World Folksonomies: Clustering in Tri-Partite Hypergraphs

Many recent Web 2.0 resource sharing applications can be subsumed under the "folksonomy" moniker. Regardless of the type of resource shared, all of these share a common structure describing the assignment of tags to resources by users. In this report, we generalize the notions of clustering and characteristic path length which play a major role in the current research on networks, where they are used to describe the small-world effects on many observable network datasets. To that end, we show that the notion of clustering has two facets which are not equivalent in the generalized setting. The new measures are evaluated on two large-scale folksonomy datasets from resource sharing systems on the web.

Trend detection in folksonomies

As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia data, from which it is difficult to extract some conceptual description of their contents. One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system.

Network properties of folksonomies

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 the systems. We consider their underlying data structures – socalled 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.

Network properties of folksonomies

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 cooccurrence 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.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 9: Metadata, Tagging and Folksonomies

In this class, we will discuss metadata as well as current phenomena such as tagging and folksonomies. Readings: Ontologies Are Us: A Unified Model of Social Networks and Semantics, P. Mika, International Semantic Web Conference, 522-536, 2005. [Web link] Optional: Folksonomies: power to the people, E. Quintarelli, ISKO Italy-UniMIB Meeting, (2005)

Privacy preserving data release for tagging recommender systems

Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called a folksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, tagging recommender systems has been confronted with serious privacy concerns because adversaries may re-identify a user and her/his sensitive information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy-preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Laplace noise to the weight of tags. We present extensive experimental results on two real world datasets, De.licio.us and Bibsonomy. While the personalization algorithm is successful in both cases, our results further suggest the private releasing algorithm can successfully retain the utility of the datasets while preserving users' identity.

Representação iterativa: um modelo para repositórios digitais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Rede de tags para recuperação da informação no contexto da Representação Iterativa

Information retrieval is a recurrent subject in search of information science. This kind of study aim to improve results in both searches on the Web and in various other digital information environment. In this context, the Iterative Representation model suggested for digital repositories, appears as a differential that changes the paradigm of self-archiving of digital objects, creating a concept of relationship between terms that link the user thought the material deposited in the digital environment. The links effect by the Iterative Representation aided Assisted Folksonomy generate a shaped structure that connects networks, vertically and horizontally, the objects deposited, relying on some kind of structure for representing knowledge of specialty areas and therefore, creating an information network based on knowledge of users. The network of information created, called the network of tags is dynamic and effective a different model of information retrieval and study of digital information repositories.Keywords Digital Repositories; Iterative Representation; Folksonomy; Folksonomy Assisted; Semantic Web; Network Tags.

Representação iterativa e folksonomia assistida para repositórios digitais

Information retrieval has been much discussed within Information Science lately. The search for quality information compatible with the users needs became the object of constant research.Using the Internet as a source of dissemination of knowledge has suggested new models of information storage, such as digital repositories, which have been used in academic research as the main form of autoarchiving and disseminating information, but with an information structure that suggests better descriptions of resources and hence better retrieval.Thus the objective is to improve the process of information retrieval, presenting a proposal for a structural model in the context of the semantic web, addressing the use of web 2.0 and web 3.0 in digital repositories, enabling semantic retrieval of information through building a data layer called Iterative Representation.The present study is characterized as descriptive and analytical, based on document analysis, divided into two parts: the first, characterized by direct observation of non-participatory tools that implement digital repositories, as well as digital repositories already instantiated, and the second with scanning feature, which suggests an innovative model for repositories, with the use of structures of knowledge representation and user participation in building a vocabulary domain. The model suggested and proposed ─ Iterative Representation ─ will allow to tailor the digital repositories using Folksonomy and also controlled vocabulary of the field in order to generate a data layer iterative, which allows feedback information, and semantic retrieval of information, through the structural model designed for repositories. The suggested model resulted in the formulation of the thesis that through Iterative Representation it is possible to establish a process of semantic retrieval of information in digital repositories.

FOLKSONOMIA: UMA ANÁLISE DE SUA OPERACIONALIDADE E SUA POSSÍVEL APLICABILIDADE NA CIÊNCIA DA INFORMAÇÃO

O artigo apresenta uma análise da operacionalidade das Folksonomias e a possibilidade de aplicação dessa ferramenta nos sistemas de organização da informação da área de Ciência da Informação. Para tanto foi realizada uma análise de coerência de tags e dos recursos disponíveis para etiquetagem em dois websites, a Last.fm e o CiteULike. Por meio dessa análise constatou-se que em ambos os websites ocorreram incoerências e discrepâncias nas tags utilizadas. Todavia, o sistema da Last.fm demonstrou-se mais funcional que o do CiteULike obtendo um desempenho melhor. Por fim, sugere-se a junção das Folksonomias às Ontologias, que permitiriam a criação de sistemas automatizados de organização de conteúdos informacionais alimentados pelos próprios usuários

Folksonomia: análise das tags no site Last.fm

A partir da web 2.0, com a maior participação do usuário, destaca-se a mudança nas mídias tradicionais, como rádio e televisão, que ganharam caráter social, permitindo a participação do público com algo mais além de só ouvir e ver, mas contribuir das mais diversas maneiras, dente elas está a folksonomia. Este trabalho teve como objetivo analisar as tags no site Last.fm por meio do modelo proposto por Sen et al. (2006) que as identifica em três categorias: fatuais, subjetivas e pessoais. Acredita-se que este estudo é importante para compreender como se dá a representação da informação em linguagem livre pelo usuário, assim como considera-se relevante a folksonomia como prática social, democrática e inclusiva que aproxima o usuário da informação. Como hipótese, considerou-se o predomínio de tags subjetivas. A pesquisa teve caráter descritivo-exploratória, e uma abordagem qualiquantitativa. A partir de uma pesquisa do IBOPE (2013), foram escolhidas 12 artistas e 12 músicas como amostra da pesquisa. Analisou-se 1109 tags, com os resultados: 759 (68%) fatuais; 232 (21%) subjetivas; 85 (8%) pessoais e 33 (3%) com tipologia não-identificada, desta maneira a hipótese de predomínio de tags subjetivas foi refutada. Como um desdobramento do modelo de Sen et al. (2006), distribuiu-se as tags em categorias descritivas, adaptadas do modelo de Laplante (2015), que mostraram que os gêneros/estilos musicais são predominantes da descrição dos itens. Conclui-se que mesmo com o predomínio de tags fatuais e de gêneros/estilos musicais, nem sempre a representação contida nessas etiquetas é fidedigna à realidade descritiva do item, contudo a folksonomia tem a contribuir com novos pontos de vista e de pensar uma informação.