Olivier Corby’s research while affiliated with Université Côte d'Azur and other places

Over recent years, we witnessed an astonishing growth in production and consumption of Linked Data (LD), which contains valuable information to support decision-making processes in various application domains. In this context, data visualization plays a decisive role in making sense of the large volumes of data created every day and in effectively communicating structures, processes, and trends in data in an accessible way. In this paper, we present LDViz, a visualization tool designed to support the exploration of knowledge graphs via multiple perspectives: (i) RDF graph/vocabulary inspection, (ii) RDF summarization, and (iii) exploratory search. We demonstrate the usage and feasibility of our approach through a set of use case scenarios showing how users can perform searches through SPARQL queries and explore multiple perspectives of the resulting data through multiple complementary visualization techniques. We also demonstrate the reach and generic aspects of our tool through an evaluation that tests the support of 419 different SPARQL endpoints.

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.

In this paper, we present the WeKG-MF Knowledge Graph constructed from open weather observations published by Météo-France institution. WeKG-MF relies on a semantic model that formalizes knowledge about meteorological observational data. The model is generic enough to be adopted and extended by meteorological data providers to publish and integrate their sources while complying with Linked Data principles. WeKG-MF offers access to a large number of meteorological variables described through spatial and temporal dimensions and thus has the potential to serve several scientific case studies from different domains including agriculture, agronomy, environment, climate change and natural disasters.KeywordsKnowledge graphSemantic modellingObservational dataLinked dataMeteorology

A large number of semantic Web knowledge bases have been developed and published on the Web. To help the user identify the knowledge bases relevant for a given problem, and estimate their usability, we propose a declarative indexing framework and an associated visualization Web application, KartoGraphI . It provides an overview of important characteristics for more than 400 knowledge bases including, for instance, dataset location, SPARQL compatibility level, shared vocabularies, etc.

To study and predict meteorological phenomenons and to include them in broader studies, the ability to represent and exchange meteorological data is of paramount importance. A typical approach in integrating and publishing such data now is to formalize a knowledge graph relying on Linked Data and semantic Web standard models and practices. In this paper, we first discuss the semantic modelling issues related to spatio-temporal data such as meteorological observational data. We motivate the reuse of a network of existing ontologies to define a semantic model in which meteorological parameters are semantically defined, described and integrated. The model is generic enough to be adopted and extended by meteorological data providers to publish and integrate their sources while complying with Linked Data principles. Finally, we present a meteorological knowledge graph of weather observations based on our proposed model, published in the form of an RDF dataset, that we produced by transforming observation records made by Météo-France weather stations. It covers a large number of meteorological variables described through spatial and temporal dimensions and thus has the potential to serve several scientific case studies from different domains including agriculture, agronomy, environment, climate change and natural disasters.KeywordsKnowledge graphSemantic modellingObservational dataLinked DataMeteorology