Gaoussou Camara

Building a Schistosomiasis Process Ontology for an Epidemiological Monitoring System

This chapter describes the design of an ontology that aims to support a monitoring system for schistosomiasis. On one hand, a domain ontology for the schistosomiasis is built to support communication and collaborative work between domain experts along the monitoring steps. The domain ontology also supports data and application integration and allows some reasoning capabilities. On the other hand, a process ontology of the schistosomiasis spreading is built for explanations and decision-making. Furthermore, the possibilities of using this process ontology for prediction are also worth considering. Here, we have focused on the design of the process ontology of infectious disease spreading and its extension to schistosomiasis. We aim to provide a formal theory in the health domain to conceptualize the processes of the infectious disease spreading and to present reasoning capabilities on the disease occurrences within a population. We emphasize on the basic entities and their relations within the complex process of infectious disease spreading. A multi-level analysis of the global dynamics is provided, taking into account biomedical, clinical and epidemiological dependences. We then propose a formalization of the infectious disease spreading process. Finally, we extend the process ontology for schistosomiasis spreading in Senegal.

Towards an Ontology-Based Framework for Data Integration: Application to Agriculture and Health Domains in Senegal

Computer-based systems have reached an advanced level in Africa during the past decade. In Senegal, the agriculture and health domain have led the way by initiating number of projects in ontology-based data integration. In this chapter, we are aiming at two main purposes. First, we present the state of the art in data integration in the agriculture and health domains in Senegal. We focus on the two latest projects: SIC-Senegal for agriculture and SIMENS (the National Medical Information System of Senegal) for the health. Second, we describe the methodological evolution of the ontology-based conceptual framework for data integration stemmed from the lessons learned along the two previous experiences. Finally, we will end this chapter by discussing the new challenges in ontology-based approach in supporting information system design in a larger scale such as climate change risk management. We also discuss the linked open data initiative in Senegal through the KOCC (Knowledge, Ontology, Community and Culture) project.

Towards Meningitis Ontology for the Annotation of Text Corpora

Ontologies are formal models that describe the semantics of domain knowledge in order to facilitate automatic processing and provide reasoning possibilities. In the context of this work, we aim at designing an ontology for assisting data and event extraction from text corpora in social medias. These items extracted will be used as input for meningitis spreading simulation within a surveillance system. In this paper, we describe the methodology we propose for building such an ontology. Our approach is based on the reuse of some NeOn scenarios and the use of an annotation tool for text corpus of the biomedical domain. This tool takes as input a text corpus on meningitis and allows us to identify relevant ontological resources. The paper is ending by presenting examples of conceptual models resulting from the application of the first step of our methodology.

Diverse perspectives on ontology: A joint report on the First IAOA Interdisciplinary Summer School on Ontological Analysis

The aim of the following report is to outline the content of the lectures given during the First IAOA Interdisciplinary Summer School on Ontological Analysis July 16--20, 2012, Trento, Italy. The report emphasizes some of the hot topics or open questions discussed during the sessions, while reflecting a variety of perspectives on the goals of ontological analysis and the importance of interdisciplinarity in dealing with certain problems.