A scalable mining of frequent quadratic concepts in d-folksonomies

Folksonomy mining is grasping the interest of web 2.0 community since it represents the core data of social resource sharing systems. However, a scrutiny of the related works interested in mining folksonomies unveils that the time stamp dimension has not been considered. For example, the wealthy number of works dedicated to mining tri-concepts from folksonomies did not take into account time dimension. In this paper, we will consider a folksonomy commonly composed of triples and we shall consider the time as a new dimension. We motivate our approach by highlighting the battery of potential applications. Then, we present the foundations for mining quadri-concepts, provide a formal definition of the problem and introduce a new efficient algorithm, called QUADRICONS for its solution to allow for mining folksonomies in time, i.e., d-folksonomies. We also introduce a new closure operator that splits the induced search space into equivalence classes whose smallest elements are the quadri-minimal generators. Carried out experiments on large-scale real-world datasets highlight good performances of our algorithm

Social tags and linked data for ontology development: a case study in the financial domain

We describe a domain ontology development approach that extracts domain terms from folksonomies and enrich them with data and vocabularies from the Linked Open Data cloud. As a result, we obtain lightweight domain ontologies that combine the emergent knowledge of social tagging systems with formal knowledge from Ontologies. In order to illustrate the feasibility of our approach, we have produced an ontology in the financial domain from tags available in Delicious, using DBpedia, OpenCyc and UMBEL as additional knowledge sources

Agent-mediated shared conceptualizations in tagging services

Some of the most remarkable innovative technologies from the Web 2.0 are the collaborative tagging systems. They allow the use of folksonomies as a useful structure for a number of tasks in the social web, such as navigation and knowledge organization. One of the main deficiencies comes from the tagging behaviour of different users which causes semantic heterogeneity in tagging. As a consequence a user cannot benefit from the adequate tagging of others. In order to solve the problem, an agent-based reconciliation knowledge system, based on Formal Concept Analysis, is applied to facilitate the semantic interoperability between personomies. This article describes experiments that focus on conceptual structures produced by the system when it is applied to a collaborative tagging service, Delicious. Results will show the prevalence of shared tags in the sharing of common resources in the reconciliation process.Ministerio de Ciencia e Innovación TIN2009-09492Ministerio de Ciencia e Innovación TIN2010-20967-C04-0

Preliminary results in tag disambiguation using DBpedia

The availability of tag-based user-generated content for a variety of Web resources (music, photos, videos, text, etc.) has largely increased in the last years. Users can assign tags freely and then use them to share and retrieve information. However, tag-based sharing and retrieval is not optimal due to the fact that tags are plain text labels without an explicit or formal meaning, and hence polysemy and synonymy should be dealt with appropriately. To ameliorate these problems, we propose a context-based tag disambiguation algorithm that selects the meaning of a tag among a set of candidate DBpedia entries, using a common information retrieval similarity measure. The most similar DBpedia en-try is selected as the one representing the meaning of the tag. We describe and analyze some preliminary results, and discuss about current challenges in this area

Review of the state of the art: discovering and associating semantics to tags in folksonomies

This paper describes and compares the most relevant approaches for associating tags with semantics in order to make explicit the meaning of those tags. We identify a common set of steps that are usually considered across all these approaches and frame our descriptions according to them, providing a unified view of how each approach tackles the different problems that appear during the semantic association process. Furthermore, we provide some recommendations on (a) how and when to use each of the approaches according to the characteristics of the data source, and (b) how to improve results by leveraging the strengths of the different approaches

Learning Structured Knowledge from Social Tagging Data A critical review of methods and techniques

For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and the Semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data

Using a social bookmarking tool for group project work online

This research investigated secondary school students’ perceptions of a social bookmarking tool for information organization, search and management. Participants include Form 1 and Form 2 (n=347) students from a Hong Kong secondary school, working on group projects over a four-month period. Students used Delicious to manage information sources for their group projects. Using a mixed-methods approach, a questionnaire and focus-group interviews examined students’ perceptions on the use of Delicious upon completion of group projects. Preliminary findings indicate positive perceptions on the usefulness of Delicious for information organization, search and management. Further analysis examined the usefulness of social bookmarking for students.postprin

Understanding and Supporting the Creation of More Effective PLE

This paper reports on the results of the first stage of an exploratory research on the learning potential of Personal Learning Environment, not simply as a technological artifact but as an instrument to support the learning process. Firstly, we present PLE as an enriched concept. Secondly we identify the roles a PLE can play to improve the quality of the learning process. Thirdly, we explore ways for optimizing the learning potential of PLE from pedagogical, technological and institutional perspectives. Before concluding, we present very preliminary results that confirm the need for a better understanding of PLE

On the Complexity of Shared Conceptualizations

In the Social Web, folksonomies and other similar knowledge organization techniques may suffer limitations due to both different users’ tagging behaviours and semantic heterogeneity. In order to estimate how a social tagging network organizes its resources, focusing on sharing (implicit) conceptual schemes, we apply an agent-based reconciliation knowledge system based on Formal Concept Analysis. This article describes various experiments that focus on conceptual structures of the reconciliation process as applied to Delicious bookmarking service. Results will show the prevalence of sharing tagged resources in order to be used by other users as recommendations.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606