Myriam Ribière

The sBook: towards social and personalized learning experiences

Despite the widespread use of Web 2.0 techniques in our entire surrounding environment, which tend to make it more social, more dynamic and driven by users, some domains have not really changed yet. This is the case for (e-)books which reading remains mainly a solitary activity - or which is done at least without appropriate collaborative tools. However, the benefits of making this activity - and especially active reading - more social and digital are huge - in particular for people having learning reading goals - leading potentially to a wide range of new services: faster access to information, possibility to interact with people sharing similar concerns or able to provide relevant explanations, determining most interesting areas in a book, or even helping users accessing faster the information that will make them progress in their learning curve. Thus, in this position paper, we describe a set of concepts and features about a sBook, which consists in making e-books more social, more communicative, in order to sustain students in a learning activity, and leverage collective intelligence from social interactions to make students learning experience more efficient.

Discovery hub: on-the-fly linked data exploratory search

Exploratory search systems help users learn or investigate a topic. The richness of the linked open data can be used to assist this task. We present a method that selects and ranks linked data resources that are semantically related to the users interest. The objective is to focus the users attention on a meaningful subset of highly informative resources. We extended spreading activation to typed graphs and coupled it with a graph sampling technique. The results selection and ranking is performed on--the-fly and doesnt require pre-processing. This allows addressing remote SPARQL endpoints. We describe first implementation on top of DBpedia. It is used by the Discovery Hub exploratory search system to select interesting resources, to support faceted browsing of the results, to provide explanations and to offer redirections to third-party services. Results of a user evaluation conclude the article.

How to Get the Recommender Out of the Lab

A personalised system is a complex system made of many interacting parts, from data ingestion to presenting the results to the users. A plethora of methods, tools, algorithms and approaches exist for each piece of such a system: many data and metadata processing methods, many user models, many filtering techniques, many accuracy metrics, many personalisation levels. In addition, a realworld recommender is a piece of an even larger and more complex environment on which there is little control: often the recommender is part of a larger application introducing constraints for the design of the recommender, e.g. the data may not be in a suitable format, or the environment may impose some architectural or privacy constraints. This can make the task of building such a recommender system daunting, and it is easy to make errors. Based on the experience of the authors and the study of other works, this chapter intends to be a guide on the design, implementation and evaluation of personalised systems. It presents the different aspects that must be studied before the design is even started, and how to avoid pitfalls, in a hands-on approach. The chapter presents the main factors to take into account to design a recommender system, and illustrates them through case studies of existing systems to help navigate in the many and complex choices that have to be faced.

Using linked data to reduce learning latency for e-book readers

Web 2.0 technologies provide an opportunity to transform learning into a social experience: social learning can directly benefit from user-generated content analysis. The e-book device is an object central to the learning process; it becomes an ideal medium to promote innovative learning tools. In this paper, we describe how we analyse user annotations in e-books using Linked Data to reduce the latency between professor knowledge, book content and student assimilation.

Composite interests' exploration thanks to on-the-fly linked data spreading activation

Exploratory search systems are built specifically to help the user in his cognitive consuming search tasks like learning or topic investigation. Some of these systems are built on the top of linked data and use semantics to provide cognitively-optimized search experiences. Thanks to their richness and to their connected nature linked data datasets can serve as a ground for advanced exploratory search. We propose to address the case of mixed interests exploration in the form of composite queries (several unitary interests combined) e.g. exploring results and make discoveries related to both The Beatles and Ken Loach.. The main contribution of this paper is the proposition of a novel method that processes linked-data for exploratory search purpose. It makes use of a semantic spreading activation algorithm coupled with a sampling technique. Its particularity is to not require any results preprocessing. Consequently this method offers a high level of flexibility for querying and allows, among others, the expression of composite interests queries on remote linked data sources. This paper also details the analysis of the algorithm behavior over DBpedia and describes an implementation: the Discovery Hub application. It is an exploratory search engine that notably supports composite queries. Finally the results of a user evaluation are presented.

Visualisation interactive de données temporelles: un aperçu de l'état de l'art

Many visualization systems have been designed and developed to address the ever-growing mass of temporal data. The multiple aspects of time (linear vs cyclic, instant vs interval, different units etc.) have been represented in different manners in existing visualization systems. A design space is thus needed to analyse and compare different visual representations used in those systems. In this article we propose a framework to describe and analyze existing temporal visual representations with emphasis on three factors: time, data and user task.

Discovery hub: a discovery engine on the top of DBpedia

This paper supports the Discovery Hub demonstration proposal. Web growth, both in size and diversity, and users growing expectations increase the need for innovative search approaches and technologies. Exploratory search systems are built specifically to help user in cognitive consuming search tasks like learning or investigation. Some of these systems are built on the top of linked data and use its semantic richness to provide cognitively-optimized search experiences. This paper presents the Discovery Hub operational prototype after detailing its Semantic Spreading Activation (SSA) algorithm. This latter processes linked data in on-the-fly and does not require partial or total results pre-processing. This on-the-fly processing offers advantages when addressing evolving linked data datasets and querying flexibility.

Indexing video segments using microblogs

This paper shows the relevance and usefulness of indexing multimedia segments thanks to associated microblog posts, for diverse multimedia applications, such as in-media social navigation, multimedia summarization or composition, or exploration of multimedia collections according to various socially-based viewpoints. We present a framework proposal to index and retrieve socialized multimedia segments (i.e. video and associated social interactions) according to several perspectives derived from text mining techniques and topic modeling. In addition, we report a user experiment validating the fundamental assumption of using social interactions to index videos.

Enhancing navigation in virtual worlds through social networks analysis

Although Virtual Worlds (VWs) are exponentially gaining popularity, they remain digitalized environments allowing to users only basic interactions and limited experience of life due mainly to the lack of realism and immersion. Thus more and more research initiatives are trying to make VWs more realistic through, for example, the use of haptic equipments and high definition drawing. This paper presents a new contribution towards enhancing VWs realism from the visual perception perspective by performing social networks analysis and conditioning avatars rendering according to social proximities.

Exploratory Search on the Top of DBpedia Chapters with the Discovery Hub Application

Discovery Hub is an exploratory search engine that helps users explore topics of interests for learning and leisure purposes. It makes use of a semantic spreading activation algorithm coupled with a sampling technique so that it does not require a preprocessing step.