Toward a General Framework for Information Fusion

National audienceDepending on the representation setting, different combination rules have been proposed for fusing information from distinct sources. Moreover in each setting, different sets of axioms that combination rules should satisfy have been advocated, thus justifying the existence of alternative rules (usually motivated by situations where the behavior of other rules was found unsatisfactory). These sets of axioms are usually purely considered in their own settings, without in-depth analysis of common properties essential for all the settings. This paper introduces core properties that, once properly instantiated, are meaningful in different representation settings ranging from logic to imprecise probabilities. The following representation settings are especially considered: classical set representation, possibility theory, and evidence theory, the latter encompassing the two other ones as special cases. This unified discussion of combination rules across different settings is expected to provide a fresh look on some old but basic issues in information fusion

Detecting Term Relationships to Improve Textual Document Sanitization

Nowadays, the publication of textual documents provides critical benefits to scientific research and business scenarios where information analysis plays an essential role. Nevertheless, the possible existence of identifying or confidential data in this kind of documents motivates the use of measures to sanitize sensitive information before being published, while keeping the innocuous data unmodified. Several automatic sanitization mechanisms can be found in the literature; however, most of them evaluate the sensitivity of the textual terms considering them as independent variables. At the same time, some authors have shown that there are important information disclosure risks inherent to the existence of relationships between sanitized and non-sanitized terms. Therefore, neglecting term relationships in document sanitization represents a serious privacy threat. In this paper, we present a general-purpose method to automatically detect semantically related terms that may enable disclosure of sensitive data. The foundations of Information Theory and a corpus as large as the Web are used to assess the degree relationship between textual terms according to the amount of information they provide from each other. Preliminary evaluation results show that our proposal significantly improves the detection recall of current sanitization schemes, which reduces the disclosure risk

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

OEGMerge: a case-based model for merging ontologies

No long ago ontology merging was a necessary activity, however, the current methods used in ontology merging present neither detailed cases nor an accurate formalization. For validating these methods, it is convenient to have a case list as complete as possible. In this paper we present the OEGMerge model, developed from the OEG (Ontological Engineering Group at UPM) experience, which describes precisely the merging casuistic and the actions to carry out in each case. In this first approach, the model covers only the taxonomy of concepts, attributes and relations

Multiuser Museum Interactives for Shared Cultural Experiences: an Agent Based Approach

Multiuser museum interactives are computer systems installed in museums or galleries which allow several visitors to interact together with digital representations of artefacts and information from the museum's collection. WeCurate is such a system, providing a multiuser curation work ow where the aim is for the users to synchronously view and discuss a selection of images, #12;nally choosing a subset of these images that the group would like to add to their group collection. The system presents two main problems: work control and group decision making. An Electronic Institution (EI) is used to model the work into scenes, where users engage in speci#12;c activities in speci#12;c scenes. A multiagent system is used to support group decision making, representing the actions of the users within the EI, where the agents advocate and support the desires of their users e.g. aggregating opinions, proposing interactions and resolutions between disagreeing group members and choosing images for discussion. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.Peer Reviewe