International audience

Open Science is based on a collaborative network to develop transparent, accessible, and shared knowledge. Open Research Data Platforms (ORDPs) are deployed to fulfill the needs for data sharing of a specific community and/or scientific discipline. The high variety of research areas creates a barrier to data sharing between research entities. To enable this research data to be found by the research entities that need it, it is necessary to establish access to different ORDPs that are unknown to these research entities. The goal of this article is to provide a quantitative analysis showing the current limitations of data sharing between ORDPs in Open Science. We then propose a solution to improve data access and sharing based on theoretical foundations and an experimental approach. We propose to extend our theoretical interoperability model, which helps us to define the necessary steps to interoperate ORDPs. We present and discuss a quantitative evaluation of ORDPs’ interoperability. Based on this exploratory study, we propose a solution that enables research entities to discover unknown ORDPs, thereby facilitating access to relevant data. This solution is the Open Science Data Network (OSDN), a decentralized, distributed, and federated network of ORDPs that integrates a query propagation process and robustness features. To enable the deployment of OSDN at an Open Science scale, we designed our solution by considering its adoption cost relative to a non-organized interoperability approach. With two ORDPs integrated into the OSDN, the adoption cost is estimated to be reduced by at least 17%. This reduction approaches 100% as the number of integrated ORDPs increases. To demonstrate the feasibility of the solution, we developed a Proof of Concept (POC) and applied it to two research projects from different domains and involving distinct research communities. For the first research project, we measured a 7% increase in the volume of accessed data and an 80% reduction in the time needed to find this data. In addition, researcher from this experiment was able to formulate new intra- and interdisciplinary research questions thanks to the newly accessed data. In the second research project, we observed an increase in data volume of up to a factor of 3968. More importantly, this process led to the discovery of new essential data that was previously missing.

With the emergence of the digital transition, the need to control the processing of digital information has significantly increased. In the EU in particular, Law Enforcement Agencies (LEAs) are caused to exchange information. In recent years, many regulations have emerged to control data processing and exchange. Texts other than the GDPR, such as the “Law Enforcement Directive (LED)”, appeared to regulate specifically their data processing. And although many new formalisms have emerged to represent legal norms and rules, few are provided with a reasoning mechanism. The explainability of the results of systems using these formalisms also remains a major issue when dealing with critical decision situations. This paper aims to propose a framework to operate formal rules from regulations and guide a user in its decision process in a situation of data processing by LEAs by focusing on both the operability of the rules through reasoning and the explainability of the results from the reasoning.

The notion of territory plays a major role in human and social sciences. Representing this spatio-temporal object and computing their changes have been tackled in various ways. However, in a historical context, most existing approaches are irrelevant as they rely on geometric data, which is not always available. Thus we designed the HHT ontology (Hierarchical Historical Territory) to represent hierarchical historical territorial divisions, without having to know their geometry. Previous versions of the ontology were limited in this aspect by the notion of building Block. This notion was expanded to enable a more flexible representation of geometry, notably based on set operators. An algorithm to detect and qualify changes was implemented using the new geometry formalisation. Six knowledge graphs regarding the evolution of French communes and the expansion of New York City were created to evaluate the use of the ontology and the proposed algorithm.

The business world has changed due to the 21st century economy, where borders have melted and trades became free. Nowadays,competition is no longer only at the local market level but also at the global level. In this context, the World Wide Web has become a major source of information for companies and professionals to keep track of their complex, rapidly changing, and competitive business environment. A lot of effort is nonetheless needed to collect and analyze this information due to information overload problem and the huge number of web pages to process and analyze. In this paper, we propose the BizRel resource, the first multilingual (French,English, Spanish, and Chinese) dataset for automatic extraction of binary business relations involving organizations from the web. This dataset is used to train several monolingual and cross-lingual deep learning models to detect these relations in texts. Our results are encouraging, demonstrating the effectiveness of such a resource for both research and business communities. In particular, we believe multilingual business relation extraction systems are crucial tools for decision makers to identify links between specific market stakeholders and build business networks which enable to anticipate changes and discover new threats or opportunities. Our work is therefore an important direction toward such tools.

Business process (BP) modelling is an active area of research due to its multiple applications. For systems that support/monitor operators to perform their tasks (i.e., tasks of a given BP), a formal representation is essential. Various BP ontologies are available to formally represent BP. In this paper, we review and compare a set of nine BP ontologies according to their ability to represent process specification and process execution in a fine-grained way to enable task monitoring. The comparison shows that, on the one hand, ontologies developed from scratch establish a clear distinction between process specification and process execution, but do not allow to represent workflow constraints required for process execution. On the other hand, most of the ontologies, that are ontological versions of existing BP modeling languages, focus only on process specifications but do not represent process execution, or mix the representation of BP specification and execution.

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.

Jihen Karoui, Farah Benamara, Véronique Moriceau, Viviana Patti, Cristina Bosco, Nathalie Aussenac-Gilles. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 2017.

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.

Many collections of structured documents are available on the web. The collection generally describes the characteristics of entities from a single type, where each page describes one entity. These documents are adequate knowledge sources for building ontologies. As they benefit from a strong and shared layout, they contain less well written text than plain text files but their architecture is very meaningful. Classical linguistic-based methods for identifying concepts and relations are no longer appropriate for analyzing them.The approach we propose in this paper exploits various properties of such documents, combining layout/formatting analysis and linguistic analysis, and using semantic annotation.

A large variety of techniques has been developed for community structure identification (CSI) including modularity optimization, graph partitioning, and hierarchical clustering. In this paper, we argue that generative models are a promising approach for community structure identification, although these models have received very little attention from CSI researchers. Following the work of Cohn and Chang on link analysis, we propose a new probabilistic model for community structure detection. The originality of our model is the use of smoothing in order to overcome the sparsity of network data. A method based on the modularity criterion is also proposed for the estimation of smoothing parameters. Experiments carried out on three real datasets show that our new model SPCE (smoothed probabilistic community explorer) significantly outperforms PHITS (probabilistic HITS).

In the article, we present Dynamo (an acronym of DYNAMic Ontologies), a tool based on an adaptive multi-agent system to construct and maintain an ontology from a domain-specific set of texts. The originality of our proposal is that the adaptative multi-agent system is used both to represent the ontology itself and to produce the ontology. This enables us to propose a system building and maintaining dynamically an ontology according to interactions with the user (also called the ontologist). We present our system and the mechanisms used to build and maintain the ontology from the texts and for the interactions with the ontologist. We also give results of the evaluation of our system.

The analysis of current approaches combining links and contents for scientific topics discovery reveals that the two sources of information (i.e. links and contents) are considered to be heterogeneous. Therefore, in this paper, we propose to integrate link and content information by exploiting the links semantics to enrich the textual content of documents. This idea is then implemented and evaluated. Experiments carried out on two real-world datasets show the good performances of our approach over state of the art techniques that combine citation and content information for scientific topics discovery.

In many domains, dimensionality reduction techniques have been shown to be very effective for elucidating the underlying semantics of data. Thus, in this paper we investigate the use of various dimensionality reduction techniques (DRTs) to extract the implicit structures hidden in the Web hyperlink connectivity. We apply and compare four DRTs, namely, principal component analysis (PCA), non-negative matrix factorization (NMF), independent component analysis (ICA) and random projection (RP). Experiments conducted on three datasets allow us to assert the following: NMF outperforms PCA and ICA in terms of stability and interpretability of the discovered structures; the well- known WebKb dataset used in a large number of works about the analysis of the hyperlink connectivity seems to be not adapted for this task and we suggest rather to use the recent Wikipedia dataset which is better suited.