Construction de modèles de données relationnels temporalisés guidée par les ontologies

Au sein d’une organisation, de même qu’entre des organisations, il y a plusieurs intervenants qui doivent prendre des décisions en fonction de la vision qu’ils se font de l’organisation concernée, de son environnement et des interactions entre les deux. Dans la plupart des cas, les données sont fragmentées en plusieurs sources non coordonnées ce qui complique, notamment, le fait de retracer leur évolution chronologique. Ces différentes sources sont hétérogènes par leur structure, par la sémantique des données qu’elles contiennent, par les technologies informatiques qui les manipulent et par les règles de gouvernance qui les contrôlent. Dans ce contexte, un système de santé apprenant (Learning Health System) a pour objectif d’unifier les soins de santé, la recherche biomédicale et le transfert des connaissances, en offrant des outils et des services pour améliorer la collaboration entre les intervenants ; l’optique sous-jacente à cette collaboration étant de fournir à un individu de meilleurs services qui soient personnalisés. Les méthodes classiques de construction de modèle de données sont fondées sur des règles de pratique souvent peu précises, ad hoc, non automatisables. L’extraction des données d’intérêt implique donc d’importantes mobilisations de ressources humaines. De ce fait, la conciliation et l’agrégation des sources sont sans cesse à recommencer parce que les besoins ne sont pas tous connus à l’avance, qu’ils varient au gré de l’évolution des processus et que les données sont souvent incomplètes. Pour obtenir l’interopérabilité, il est nécessaire d’élaborer une méthode automatisée de construction de modèle de données qui maintient conjointement les données brutes des sources et leur sémantique. Cette thèse présente une méthode qui permet, une fois qu’un modèle de connaissance est choisi, la construction d’un modèle de données selon des critères fondamentaux issus d’un modèle ontologique et d’un modèle relationnel temporel basé sur la logique des intervalles. De plus, la méthode est semi- automatisée par un prototype, OntoRelα. D’une part, l’utilisation des ontologies pour définir la sémantique des données est un moyen intéressant pour assurer une meilleure interopérabilité sémantique étant donné que l’ontologie permet d’exprimer de façon exploitable automatiquement différents axiomes logiques qui permettent la description de données et de leurs liens. D’autre part, l’utilisation d’un modèle relationnel temporalisé permet l’uniformisation de la structure du modèle de données, l’intégration des contraintes temporelles ainsi que l’intégration des contraintes du domaine qui proviennent des ontologies.Within an organization, many stakeholders must make decisions based on their vision of the organization, its environment, and the interactions between these two. In most cases, the data are fragmented in several uncoordinated sources, making it difficult, in particular, to trace their chronological evolution. These different sources are heterogeneous in their structure, in the semantics of the data they contain, in the computer technologies that manipulate them, and in the governance rules that control them. In this context, a Learning Health System aims to unify health care, biomedical research and knowledge transfer by providing tools and services to enhance collaboration among stakeholders in the health system to provide better and personalized services to the patient. The implementation of such a system requires a common data model with semantics, structure, and consistent temporal traceability that ensures data integrity. Traditional data model design methods are based on vague, non-automatable best practice rules where the extraction of data of interest requires the involvement of very important human resources. The reconciliation and the aggregation of sources are constantly starting over again because not all needs are known in advance and vary with the evolution of processes and data are often incomplete. To obtain an interoperable data model, an automated construction method that jointly maintains the source raw data and their semantics is required. This thesis presents a method that build a data model according to fundamental criteria derived from an ontological model, a relational model and a temporal model based on the logic of intervals. In addition, the method is semi-automated by an OntoRelα prototype. On the one hand, the use of ontologies to define the semantics of data is an interesting way to ensure a better semantic interoperability since it automatically expresses different logical axioms allowing the description of data and their links. On the other hand, the use of a temporal relational model allows the standardization of data model structure and the integration of temporal constraints as well as the integration of domain constraints defines in the ontologies

Agnostic content ontology design patterns for a multi-domain ontology

This research project aims to solve the semantic heterogeneity problem. Semantic heterogeneity mimics cancer in that semantic heterogeneity unnecessarily consumes resources from its host, the enterprise, and may even affect lives. A number of authors report that semantic heterogeneity may cost a significant portion of an enterprise’s IT budget. Also, semantic heterogeneity hinders pharmaceutical and medical research by consuming valuable research funds. The RA-EKI architecture model comprises a multi-domain ontology, a cross-industry agnostic construct composed of rich axioms notably for data integration. A multi-domain ontology composed of axiomatized agnostic data model patterns would drive a cognitive data integration application system usable in any industry sector. This project’s objective is to elicit agnostic data model patterns here considered as content ontology design patterns. The first research question of this project pertains to the existence of agnostic patterns and their capacity to solve the semantic heterogeneity problem. Due to the theory-building role of this project, a qualitative research approach constitutes the appropriate manner to conduct its research. Contrary to theory testing quantitative methods that rely on well-established validation techniques to determine the reliability of the outcome of a given study, theorybuilding qualitative methods do not possess standardized techniques to ascertain the reliability of a study. The second research question inquires on a dual method theory-building approach that may demonstrate trustworthiness. The first method, a qualitative Systematic Literature Review (SLR) approach induces the sought knowledge from 69 retained publications using a practical screen. The second method, a phenomenological research protocol elicits the agnostic concepts from semi-structured interviews involving 22 senior practitioners with 21 years in average of experience in conceptualization. The SLR retains a set of 89 agnostic concepts from 2009 through 2017. The phenomenological study in turn retains 83 agnostic concepts. During the synthesis stage for both studies, data saturation was calculated for each of the retained concepts at the point where the concepts have been selected for a second time. The quantification of data saturation constitutes an element of the trustworthiness’s transferability criterion. It can be argued that this effort of establishing the trustworthiness, i.e. credibility, dependability, confirmability and transferability can be construed as extensive and this research track as promising. Data saturation for both studies has still not been reached. The assessment performed in the course of the establishment of trustworthiness of this project’s dual method qualitative research approach yields very interesting findings. Such findings include two sets of agnostic data model patterns obtained from research protocols using radically different data sources i.e. publications vs. experienced practitioners but with striking similarities. Further work is required using exactly the same protocols for each of the methods, expand the year range for the SLR and to recruit new co-researchers for the phenomenological protocol. This work will continue until these protocols do not elicit new theory material. At this point, new protocols for both methods will be designed and executed with the intent to measure theoretical saturation. For both methods, this entails in formulating new research questions that may, for example, focus on agnostic themes such as finance, infrastructure, relationships, classifications, etc. For this exploration project, the road ahead involves the design of new questionnaires for semi-structured interviews. This project will need to engage in new knowledge elicitation techniques such as focus groups. The project will definitely conduct other qualitative research methods such as research action for eliciting new knowledge and know-how from actual development and operation of an ontology-based cognitive application. Finally, a mixed methods qualitative-quantitative approach would prepare the transition toward theory testing method using hypothetico-deductive techniques