Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

Collaborative semantic web browsing with Magpie

Web browsing is often a collaborative activity. Users involved in a joint information gathering exercise will wish to share knowledge about the web pages visited and the contents found. Magpie is a suite of tools supporting the interpretation of web pages and semantically enriched web browsing. By automatically associating an ontology-based semantic layer to web resources, Magpie allows relevant services to be invoked as well as remotely triggered within a standard web browser. In this paper we describe how Magpie trigger services can provide semantic support to collaborative browsing activities

Division of labour and sharing of knowledge for synchronous collaborative information retrieval

Synchronous collaborative information retrieval (SCIR) is concerned with supporting two or more users who search together at the same time in order to satisfy a shared information need. SCIR systems represent a paradigmatic shift in the way we view information retrieval, moving from an individual to a group process and as such the development of novel IR techniques is needed to support this. In this article we present what we believe are two key concepts for the development of effective SCIR namely division of labour (DoL) and sharing of knowledge (SoK). Together these concepts enable coordinated SCIR such that redundancy across group members is reduced whilst enabling each group member to benefit from the discoveries of their collaborators. In this article we outline techniques from state-of-the-art SCIR systems which support these two concepts, primarily through the provision of awareness widgets. We then outline some of our own work into system-mediated techniques for division of labour and sharing of knowledge in SCIR. Finally we conclude with a discussion on some possible future trends for these two coordination techniques

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

Modeling social information skills

In a modern economy, the most important resource consists in\ud human talent: competent, knowledgeable people. Locating the right person for\ud the task is often a prerequisite to complex problem-solving, and experienced\ud professionals possess the social skills required to find appropriate human\ud expertise. These skills can be reproduced more and more with specific\ud computer software, an approach defining the new field of social information\ud retrieval. We will analyze the social skills involved and show how to model\ud them on computer. Current methods will be described, notably information\ud retrieval techniques and social network theory. A generic architecture and its\ud functions will be outlined and compared with recent work. We will try in this\ud way to estimate the perspectives of this recent domain

Interoperability in the OpenDreamKit Project: The Math-in-the-Middle Approach

OpenDreamKit --- "Open Digital Research Environment Toolkit for the Advancement of Mathematics" --- is an H2020 EU Research Infrastructure project that aims at supporting, over the period 2015--2019, the ecosystem of open-source mathematical software systems. From that, OpenDreamKit will deliver a flexible toolkit enabling research groups to set up Virtual Research Environments, customised to meet the varied needs of research projects in pure mathematics and applications. An important step in the OpenDreamKit endeavor is to foster the interoperability between a variety of systems, ranging from computer algebra systems over mathematical databases to front-ends. This is the mission of the integration work package (WP6). We report on experiments and future plans with the \emph{Math-in-the-Middle} approach. This information architecture consists in a central mathematical ontology that documents the domain and fixes a joint vocabulary, combined with specifications of the functionalities of the various systems. Interaction between systems can then be enriched by pivoting off this information architecture.Comment: 15 pages, 7 figure

The LAB@FUTURE Project - Moving Towards the Future of E-Learning

This paper presents Lab@Future, an advanced e-learning platform that uses novel Information and Communication Technologies to support and expand laboratory teaching practices. For this purpose, Lab@Future uses real and computer-generated objects that are interfaced using mechatronic systems, augmented reality, mobile technologies and 3D multi user environments. The main aim is to develop and demonstrate technological support for practical experiments in the following focused subjects namely: Fluid Dynamics - Science subject in Germany, Geometry - Mathematics subject in Austria, History and Environmental Awareness – Arts and Humanities subjects in Greece and Slovenia. In order to pedagogically enhance the design and functional aspects of this e-learning technology, we are investigating the dialogical operationalisation of learning theories so as to leverage our understanding of teaching and learning practices in the targeted context of deployment