Abstract
Rare diseases pose particular challenges to patients, families, caregivers, clinicians and researchers. Due to the scarce availability of information and their disintegration, in recent years we are witnessing a strong growth of patient communities on social platforms such as Facebook. Although the data generated in this context are of high value, the currently existing ontologies and resources tend to ignore them. The work presented in this paper studies how to extract knowledge from the large availability of unstructured text generated by the users over time, in order to represent it in an organized way and to make logical reasoning above. Starting from the awareness of the need to integrate different methodologies in complex domains, the research shows a combined use of Text Mining and Semantic Web techniques. In particular, we describe the basis of a novel approach for Knowledge Graph Learning with the aim of introducing a patient-centered vision into the world of Linked Open Data. By identifying and representing correlations between concepts of interest, we show how it is possible to answer patients’ questions and provide them with an additional tool for decision making. The outlined contribute minimizes costs through automatic data retrieval and increases the productivity of investigators.
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Model used to express the frequency and the provenance of associations, in an ontological design context.
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Frisoni, G., Moro, G., Carbonaro, A. (2021). Towards Rare Disease Knowledge Graph Learning from Social Posts of Patients. In: Visvizi, A., Lytras, M.D., Aljohani, N.R. (eds) Research and Innovation Forum 2020. RIIFORUM 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-62066-0_44
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