Entity Matching (EM) automates the discovery of identity links between entities within different Knowledge Graphs (KGs). Link keys are crucial for EM, serving as rules allowing to identify identity links across different KGs, possibly described using different ontologies. However, the approach for extracting link keys struggles to scale on large KGs. While embedding-based EM methods efficiently handle large KGs they lack explainability. This paper proposes a novel hybrid EM approach to guarantee the scalability link key extraction approach and improve the explainability of embedding-based EM methods. First, embedding-based EM approaches are used to sample the KGs based on the identity links they generate, thereby reducing the search space to relevant sub-graphs for link key extraction. Second, rules (in the form of link keys) are extracted to explain the generation of identity links by the embedding-based methods. Experimental results demonstrate that the proposed approach allows link key extraction to scale on large KGs, preserving the quality of the extracted link keys. Additionally, it shows that link keys can improve the explainability of the identity links generated by embedding-methods, allowing for the regeneration of 77% of the identity links produced for a specific EM task, thereby providing an approximation of the reasons behind their generation.

This article presents the achievements of the Candela project. This project aims to develop a platform and new algorithms for the handling, analysis and interpretation of earth observation data. The platform is hosted on the CREODIAS cloud ensuring the proximity of data and its processing. To ensure good performances the platform can scale up or down its computing resources. New algorithms based on machine learning methods for change detection and classification have been developed in the project. The results of these new algorithms are transformed into semantic data used to enrich earth observation products and provide new ways of exploitation. Finally, an end-to-end use of the platform is presented with a use case study of the impact of intense meteorological events on vineyards.

This paper presents an integration process of open data and Earth Observation (EO) data for supporting EO semantic search. This process relies on an ontology that describes spatial and temporal dimensions of data. The resulting dataset provides rich contextual information about EO and makes possible the search of EO data according to this contextual information through a semantic search interface. The approach is illustrated on the integration of different datasets: change detection, administrative unit, land register and land cover.

Extracting hypernym relations from text is one of the key steps in the automated construction and enrichment of semantic resources. The state of the art offers a large varierty of methods (linguistic, statistical, learning based, hybrid). This variety could be an answer to the need to process each corpus or text fragment according to its specificities (e.g. domain granularity, nature, language, or target semantic resource). Moreover, hypernym relation may take different linguistic forms. The aim of this paper is to study the behaviour of a supervised learning approach to extract hypernym relations whatever the way they are expressed, and to evaluate its ability to capture regularities from the corpus, without human intervention. We apply a distant supervised learning algorithm on a sub-set of Wikipedia in French made of disambiguation pages where we manually annotated hypernym relations. The learned model obtained a F-measure of 0.67, outperforming lexico-syntactic pattern matching used as baseline.

This paper presents an intelligent and adaptive virtual environment, which has its structure and presentation customized according to users' interests and preferences (represented in a user model) and in accordance with insertion and removal of contents in this environment. An automatic content categorization process is applied to create content models, used in the spatial organization of the contents in the environment. An intelligent agent assists users during navigation in the environment and retrieval of relevant information. In order to validate our proposal, a prototype of a distance learning environment, used to make educational content available, was developed.

This work presents an intelligent virtual environment, called AdapTIVE (adaptive three-dimensional intelligent and virtual environment), which has its structure and presentation customized according to users' interests and preferences (represented in a user model) and in accordance with insertion and removal of contents in this environment. An automatic content categorization process is applied to create content models, used in the spatial organization of the contents in the environment. An intelligent agent assists users during navigation in the environment and retrieval of relevant information. This is a promising approach for new and advanced forms of education, entertainment and e-commerce. In order to validate our approach, a case study of an e-commerce environment is presented.