The work carried out during these two years can be explained in three parts. The first part was related to specifying a temporal framework for temporal representations and the associated engineering process to create temporal ontologies or convert static ontologies to their temporal versions. The main scientific achievement was describing the semantics involving different temporal aspects, such as the uncertainty of temporal relations and classes. Moreover, this proposal followed the OWL standards, so our approach can be integrated into the main ontology frameworks and semantic reasoning engines. Thus, our main contribution was to provide ontological support for domains that require more complex temporal representations, such as uncertain time and time relations, considering both temporal intervals and moments. The approach's viability and generalizability were demonstrated through practical applications using case studies in different health domains, such as mental health, aging, and sleep quality issues.
The second part of our project employed new forms of reasoning (inductive reasoning), which receive descriptions modelled according to our ontological proposal as input. For this work, we considered the state-of-the-art inductive methods, represented by the deep learning transformers architectures. Our review on this topic (paper submitted for review) shows that their definition only supports sequential rather than temporal information (e.g. events duration and temporal distance). Thus, our main contribution was to formalise such limitations and propose initial research directions to cover these limitations.
The third part was related to the work conducted during the secondment. The proposal was to adapt and evaluate the algorithms developed within the Onto-mQoL project on EHR-based datasets provided by the Imperial College London (Computational Oncology Group, Department of Surgery & Cancer, Faculty of Medicine). These activities involved, for example, the specialisation of our ontology to cover special aspects of cancer disease and its dataset population (e.g. ageing and gender).
As main results, we had three scientific publications in high-impact journals and a conference paper published at the IEEE International Conference on Biomedical and Health Informatics. We still have three journal papers in the review process. If such papers are not accepted, they will be deposited in open-access repositories. All these papers have the EU funding reference.
The exploitation and dissemination of the project were mainly conducted by means of scientific and general public presentations at local, regional, and international events: University of Geneva Data Science Day 2022, Conference d Universitarire de Suisse Occidentale (CUSO) Winter School 2022, ISOQOL Annual Conference 2022, and IEEE-EMBS International Conference on Biomedical and Health Informatics. Moreover, the project had an initial web page for promoting its progress. Such a page had an interactive simulation where visitors could play with the temporal reasoning engine, a blog with project updates, and a video demonstrating an application (versions with subtitles in English and French). A second version of this web page (current version) is hosted on the university's main server (
https://www.unige.ch/onto-mqol/(odnośnik otworzy się w nowym oknie)) and it contains all the supplementary material indicated in the papers. A project summary is also hosted on the projects page of our laboratory (
https://www.qualityoflifetechnologies.com/research-project/onto-mqol/(odnośnik otworzy się w nowym oknie)).
