Knowledge hypergraph based-approach for multi-source data integration and querying : Application for Earth Observation domain

Early warning against natural disasters to save lives and decrease damages has drawn increasing interest to develop systems that observe, monitor, and assess the changes in the environment. Over the last years, numerous environmental monitoring systems and Earth Observation (EO) programs were implemented. Nevertheless, these systems generate a large amount of EO data while using different vocabularies and different conceptual schemas. Accordingly, data resides in many siloed systems and are mainly untapped for integrated operations, insights, and decision making situations. To overcome the insufficient exploitation of EO data, a data integration system is crucial to break down data silos and create a common information space where data will be semantically linked. Within this context, we propose a semantic data integration and querying approach, which aims to semantically integrate EO data and provide an enhanced query processing in terms of accuracy, completeness, and semantic richness of response. . To do so, we defined three main objectives. The first objective is to capture the knowledge of the environmental monitoring domain. To do so, we propose MEMOn, a domain ontology that provides a common vocabulary of the environmental monitoring domain in order to support the semantic interoperability of heterogeneous EO data. While creating MEMOn, we adopted a development methodology, including three fundamental principles. First, we used a modularization approach. The idea is to create separate modules, one for each context of the environment domain in order to ensure the clarity of the global ontology’s structure and guarantee the reusability of each module separately. Second, we used the upper-level ontology Basic Formal Ontology and the mid-level ontologies, the Common Core ontologies, to facilitate the integration of the ontological modules in order to build the global one. Third, we reused existing domain ontologies such as ENVO and SSN, to avoid creating the ontology from scratch, and this can improve its quality since the reused components have already been evaluated. MEMOn is then evaluated using real use case studies, according to the Sahara and Sahel Observatory experts’ requirements. The second objective of this work is to break down the data silos and provide a common environmental information space. Accordingly, we propose a knowledge hypergraphbased data integration approach to provide experts and software agents with a virtual integrated and linked view of data. This approach generates RML mappings between the developed ontology and metadata and then creates a knowledge hypergraph that semantically links these mappings to identify more complex relationships across data sources. One of the strengths of the proposed approach is it goes beyond the process of combining data retrieved from multiple and independent sources and allows the virtual data integration in a highly semantic and expressive way, using hypergraphs. The third objective of this thesis concerns the enhancement of query processing in terms of accuracy, completeness, and semantic richness of response in order to adapt the returned results and make them more relevant and richer in terms of relationships. Accordingly, we propose a knowledge-hypergraph based query processing that improves the selection of sources contributing to the final result of an input query. Indeed, the proposed approach moves beyond the discovery of simple one-to-one equivalence matches and relies on the identification of more complex relationships across data sources by referring to the knowledge hypergraph. This enhancement significantly showcases the increasing of answer completeness and semantic richness. The proposed approach was implemented in an open-source tool and has proved its effectiveness through a real use case in the environmental monitoring domain

An ontology-based monitoring system for multi-source environmental observations

Multi-source observed data are generally characterized by their syntactic, structural and semantic heterogeneities. A key challenge is the semantic interoperability of these data. In this context, we propose an ontology-based system that supports environmental monitoring. Our contributions could be resumed around 1) the construction of an ontology which allows to represent the knowledge and reuse it in a real-world way, 2) the guarantee of the semantic interoperability of ontological modules since the proposed ontology is based on the upper level ontology Basic Formal Ontology (BFO) 3) the modularity of the proposed ontology in order to facilitate its reuse and evolution. The proposed ontology has been implemented and evaluated using quality metrics. We also present a real use case study that demonstrates how the proposed ontology allows implicit knowledge generation

PREDICAT: a semantic service-oriented platform for data interoperability and linking in earth observation and disaster prediction

The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service-oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS)

Une approche basée sur l'hypergraphe de connaissances pour l'intégration de données multisource : Application à l'observation de la terre

Les dégâts humains et matériels engendrés par les catastrophes naturelles, avaient suscité un intérêt grandissant pour le développement des systèmes d’observation et de surveillance de l’environnement, sans pour autant mettre en exergue, la collaboration et l’échange comme principal point d’une efficace prévention des catastrophes. De tels systèmes génèrent des données hétérogènes et cloisonnées dans des silos. A défaut d’une vision globale des données disponibles, les experts éprouvent des difficultés à accéder, manipuler et comprendre ces données multi-source. Afin de remédier à cette insuffisance d’exploitation, un système d'intégration de données est essentiel pour briser les silos de données et créer un espace commun d'information où les données seront liées sémantiquement. C’est dans cet ordre d’idées que nous proposons une approche sémantique d'intégration et d'interrogation des données multisources. Pour ce faire, nous avons défini trois principaux objectifs. Le premier objectif est de formaliser les connaissances liées au domaine de l’environnement afin d'assurer une interopérabilité sémantique entre les données multi-source. Ainsi, nous avons proposé MEMOn, une ontologie de domaine qui fournit un vocabulaire commun couvrant le domaine de l’environnement. Nous avons adopté une méthodologie agile basée sur la modularisation, l’alignement avec une ontologie de haut niveau et la réutilisation des ontologies existantes. La modularisation consiste à développer des modules ontologiques séparés. Chaque module présente un contexte spécifique du domaine de l’environnement et ce dans le but d’assurer la clarté de la structure de l’ontologie globale. De plus, nous avons utilisé l’ontologie de haut niveau Basic Formal Ontology et les ontologies intermédiaires Common Core Ontologies afin de faciliter l’intégration des modules ontologiques développés pour créer MEMOn. Aussi, nous avons réutilisé des ontologies de domaine existantes telles que ENVO et SSN afin d’éviter de créer notre ontologie à partir de zéro. MEMOn est ensuite évaluée à l'aide de cas d'utilisation réelles et conformément aux exigences des experts. Le deuxième objectif de ce travail est de briser les silos de données et de fournir un espace commun d'information sur l'environnement où les données pourraient être liées sémantiquement. En conséquence, nous proposons une approche sémantique d'intégration virtuelle des données basée sur l'hypergraphe afin de fournir aux experts une vue intégrée et liée des données. L’approche consisite à génrer des mappings RML entre l'ontologie et les métadonnées et à créer ensuite un hypergraphe de connaissances qui relie sémantiquement ces mappings afin d’identifier des relations plus complexes entre les données. Un des atouts de l'approche proposée est qu'elle va au-delà du processus de combinaison de données extraites de sources indépendantes pour assurer une intégration de données hautement sémantique et expressive. Le troisième objectif de cette thèse concerne l'amélioration du traitement des requêtes en termes de précision et de complétude des résultats afin d'adapter les résultats renvoyés et les rendre plus pertinents et plus riches termes de relations. En conséquence, nous avons développé une approche de traitement des requêtes basée sur l'hypergraphe de connaissances qui améliore la tâche de sélection des sources contribuant au résultat final d'une requête SPARQL saisie. En effet, l'approche proposée transcende la simple découverte de correspondances entre la requête et les schémas de sources et assure l'identification de correspondances plus complexes avec les sources de données en se référant à l'hypergraphe de connaissances. Sur la base de ces résultats, d'autres étapes du traitement de la requête, y compris la réécriture de la requête et l'évaluation de la requête, sont effectuées. Notre approche est concrétisée par le développement d’un outil dont l’efficacité a été prouvée moyennant l’évaluation d’un cas réel.Early warning against natural disasters to save lives and decrease damages has drawn increasing interest to develop systems that observe, monitor, and assess the changes in the environment. Over the last years, numerous environmental monitoring systems and Earth Observation (EO) programs were implemented. Nevertheless, these systems generate a large amount of EO data while using different vocabularies and different conceptual schemas. Accordingly, data resides in many siloed systems and are mainly untapped for integrated operations, insights, and decision making situations. To overcome the insufficient exploitation of EO data, a data integration system is crucial to break down data silos and create a common information space where data will be semantically linked. Within this context, we propose a semantic data integration and querying approach, which aims to semantically integrate EO data and provide an enhanced query processing in terms of accuracy, completeness, and semantic richness of response. . To do so, we defined three main objectives. The first objective is to capture the knowledge of the environmental monitoring domain. To do so, we propose MEMOn, a domain ontology that provides a common vocabulary of the environmental monitoring domain in order to support the semantic interoperability of heterogeneous EO data. While creating MEMOn, we adopted a development methodology, including three fundamental principles. First, we used a modularization approach. The idea is to create separate modules, one for each context of the environment domain in order to ensure the clarity of the global ontology’s structure and guarantee the reusability of each module separately. Second, we used the upper-level ontology Basic Formal Ontology and the mid-level ontologies, the Common Core ontologies, to facilitate the integration of the ontological modules in order to build the global one. Third, we reused existing domain ontologies such as ENVO and SSN, to avoid creating the ontology from scratch, and this can improve its quality since the reused components have already been evaluated. MEMOn is then evaluated using real use case studies, according to the Sahara and Sahel Observatory experts’ requirements. The second objective of this work is to break down the data silos and provide a common environmental information space. Accordingly, we propose a knowledge hypergraphbased data integration approach to provide experts and software agents with a virtual integrated and linked view of data. This approach generates RML mappings between the developed ontology and metadata and then creates a knowledge hypergraph that semantically links these mappings to identify more complex relationships across data sources. One of the strengths of the proposed approach is it goes beyond the process of combining data retrieved from multiple and independent sources and allows the virtual data integration in a highly semantic and expressive way, using hypergraphs. The third objective of this thesis concerns the enhancement of query processing in terms of accuracy, completeness, and semantic richness of response in order to adapt the returned results and make them more relevant and richer in terms of relationships. Accordingly, we propose a knowledge-hypergraph based query processing that improves the selection of sources contributing to the final result of an input query. Indeed, the proposed approach moves beyond the discovery of simple one-to-one equivalence matches and relies on the identification of more complex relationships across data sources by referring to the knowledge hypergraph. This enhancement significantly showcases the increasing of answer completeness and semantic richness. The proposed approach was implemented in an open-source tool and has proved its effectiveness through a real use case in the environmental monitoring domain

Semantic-based Data Integration and Mapping Maintenance: Application to Drugs Domain

International audienc

Identification of Counterfeit Drugs Based on Traceability Ontology and Blockchain

International audienceIn the context of drug traceability, counterfeit drugs have a significant influence on customer health and trust towards the manufacturers. Therefore, the awareness of drug safety has led to a considerable need for improved traceability in the supply chain. To do so, we have proposed an approach combining blockchain and semantic web technologies to ensure drug traceability in a secure and trustworthy manner. The main contribution of this work is the construction of the Drug Traceability Ontology, then using this ontology alongside with the blockchain technology to check the drugs authenticity

A semantic blockchain-based system for drug traceability

International audienceDrug traceability is currently a very challenging area given the complexity of several issues, including drug quality and counterfeit medications. The counterfeited drugs have a major impact on human life, treatment outcomes and economic burden. To deal with these issues, we propose a semantic blockchain-based system for drug traceability that aims at detecting counterfeit drugs in order to improve the patients’ safety and quality of life as well as eliminating manufacturers’ potential loss and increasing their revenue. Our proposal is based on blockchain and semantic web technologies to enhance the representation capability of data in the pharmaceutical supply chain

Epidemiology and disease burden of tuberculosis in south of Tunisia over a 22-year period: Current trends and future projections.

BackgroundTuberculosis (TB) is a public health problem worldwide. Characterizing its trends over time is a useful tool for decision-makers to assess the efficiency of TB control programs. We aimed to give an update on the current chronological trends of TB in Southern Tunisia from 1995 to 2016 and to estimate future trajectories of TB epidemic by 2030.MethodsWe retrospectively collected data of all notified TB new cases by the Center of Tuberculosis Control between 1995 and 2016 in South of Tunisia. Joinpoint Regression Analysis was performed to analyze chronological trends and annual percentage changes (APC) were estimated.ResultsIn the past 22 years, a total of 2771 cases of TB were notified in Southern Tunisia. The annual incidence rate of TB was 13.91/100,000 population/year. There was a rise in all forms of TB incidence (APC = 1.63) and in extrapulmonary tuberculosis (EPTB) (APC = 2.04). The incidence of TB increased in children and adult females between 1995 and 2016 (APC = 4.48 and 2.37, respectively). The annual number of TB declined in urban districts between 2004 and 2016 (APC = -2.85). Lymph node TB cases increased (APC = 4.58), while annual number of urogenital TB decreased between 1995 and 2016 (APC = -3.38). Projected incidence rates would increase to 18.13 and 11.8/100,000 population in 2030 for global TB and EPTB, respectively.ConclusionsOur study highlighted a rise in all forms of TB and among high-risk groups, notably children, females and lymph node TB patients in the last two decades and up to the next one