Time-evolving knowledge graphs based on Poirot: dynamic representation of patients' voices

Nowadays people are spending more and more time online: this is a permanent change that leads to a huge amount of diversified data like never before which needs to be managed to extrapolate knowledge from it. This also involves social media which produces free textual information very difficult to process, but occasionally very useful. For instance, in the field of rare diseases, our specific testing context could lead to the possibility to organize the voice of patients and of caregivers, difficult to gather otherwise. People who are affected by a rare disease often strive to find enough information about it. Indeed, not much material is available online and the number of doctors qualified for those specific diseases is quite limited. Social networks become then the best place to exchange ideas and opinions. The main difficulty in finding useful information on social networks though is that text gets lost quickly and it's not straightforward to give a semantic structure to it and dynamically evolve this representation over time. In literature, there are some techniques that manage to transform unstructured data into useful information, extracting them using artificial intelligence. These techniques are often well expressive and are able to precisely convert data into knowledge, but they are not directly connected to text sources nor to a system that stores and allows to update the extrapolated information. Consequently, they are not well automated in incrementally keeping information up-to-date as new text is provided, resulting in the need for a mechanical process to do it. The contribution proposed in this thesis focuses on how to use these technologies to maintain information in order over time, enhancing their usability and freshness. It consists of a system that connects the text source providers to the built knowledge graph, which contains the knowledge acquired and updated.