Πλοήγηση ανά Επιβλέποντα "Pappas, Dimitris"

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Neural graph representations and their application to link prediction
(2020) Kotitsas, Sotiris; Κοτίτσας, Σωτήριος; Koutsopoulos, Iordanis; Papageorgiou, Haris; Androutsopoulos, Ion; Pappas, Dimitris
In this thesis, we experiment with the task of Link Prediction using Network Embedding(ne) methods. ne methods map network nodes to low-dimensional feature vectors and have wide applications in network analysis and bioinformatics. We consider separately the task of Link Prediction in graphs with only one type of relationship and in graphs with more than one type of relationship. The ultimate goal is to create methods capable of making novel predictions and helping in the Biomedical domain, e.g. covid-19 related predictions. To that end, we create a biomedical dataset containing Coronavirus related information complemented by entities and relationships acquired from the umls ontology. Secondly, we note that the ne methods can be categorized to methods that utilize only the structure of the graphs and to methods that also try to exploit metadata associated with graphs, e.g. textual descriptors of the nodes. We utilize the idea of incorporating textual with structural information and propose several novel architectures which try to tackle the problem of simple and multi-relational link prediction. We evaluate these approaches to several benchmark datasets and also show that our multi-relational methods are competitive against the state-of-the-art in two benchmark datasets. We also show that our approach yields the same results and even outperforms the state-of-the-art in some metrics in our COVID-related graph. Finally, we do predictions regarding the covid-19concept and try to show their novelty, by examining if we are discovering information that had not been published when the COVID-related graph was constructed.