The Web of Linked Open Data (LOD) provides access to a great number of dynamic datasets containing valuable information to support decision-making processes in diverse application domains while being publicly accessible and up-to-date. While information visualization techniques are useful to explore, analyze, and explain relationships within LOD data, the existing tools are limited to visualizing a single dataset at a time and, often, use static and preprocessed data. In this article, we leverage the linked aspect of LOD to support the dynamic integration of data into a visualization system by connecting views and distributed LOD datasets using the so-called follow-up queries. We demonstrate how our approach uses dynamic SPARQL queries to integrate external data into the exploration flow through visualization techniques and enrich the ongoing analysis. We ran a semi-structured interview to assess the usefulness of our approach, which results were encouraging while showing its relevance to explore big linked data.

Association rule mining often leads the analyst into a rough rummaging process to identify rules that are relevant to understand specific problems. We propose a visualization interface to assist the rule selection process and evaluate it on an RDF knowledge graph derived from the COVID-19 Open Research Dataset. The user interface supports data exploration with focus on the overview of rules through a scatter plot, subsets of rules through a chord diagram chart, and itemsets through an association graph which is dynamically created by entering an item of interest (i.e. a named entity). Further, the analyst can interactively recover a list of publications containing the named entities involved in a particular rule. Among the original aspects of our approach, we highlight the representation of attributes describing measures of interest (i.e. confidence and interestingness), a visual indication of existence (or not) of symmetry in association rules, the exploration of subsets of rules according to clusters of publications and named entities, and an interactive prompting that aims at expanding the discovery of named entities within selected association rules. We assess our approach through a semi-structured interview involving experts in the domains of data mining and biomedicine, whose feedback could assist the refinement of the visual and interaction tools.

Retrieving specific categories of images among billions of images usually requires an annotation step. Unfortunately, keywords-based techniques suffer from the semantic gap existing between a semantic concept and its digital representation. Content Based Image Retrieval (CBIR) systems tackle this issue simply considering semantic proximities can be mapped to similarities in the image space. Introducing relevance feedbacks involves the user in the task, but extends the annotation step.

In the context of the Palette project aimed at enhancingallindividual and organizational learning in Communities of Practice (CoPs), we are developing Knowledge Management (KM) services. Our approach is based on an ontology dedicated to CoPs and built from analysis of information sources about eleven CoPs available in Palette project. This ontology aims both at modeling the members of the CoP and at annotating the CoP knowledge resources. The paper describes our method for building this ontology, its structure and contents and it analyses our experience feedback from the cooperative building of this ontology.

This article † is a survey of some methods, techniques and tools aimed at managing corporate knowledge from a corporate memory designer's perspective. In particular, it analyses problems and solutions related to the following steps: detection of needs of corporate memory, construction of the corporate memory, its diffusion (specially using the Internet technologies), use, evaluation and evolution