3IA PhD/Postdoc Seminar #14

A systematic approach to identify the information captured by Knowledge Graph Embeddings

Abstract: 
In the last decade Knowledge Graphs have undergone an impressive expansion, mainly due to their extensive use in AI-related applications, such as query answering or recommender systems. This growth has been powered by the expanding landscape of Graph Embedding techniques, which facilitate the manipulation of the vast and sparse information described by Knowledge Graphs. Graph Embedding algorithms create a low-dimensional vector representation of the elements in the graph, i.e. nodes and edges, suitable as input for Machine Learning tasks. Although their effectiveness has been proved on many occasions and for many contexts, the interpretability of such vector representations remains an open issue. In our work, we aim to tackle this issue by providing a systematic approach to decode and make sense of the knowledge captured by Graph Embeddings. We proposed a technique for verifying whether Graph Embeddings are able to encode certain properties of the graph elements and we present a categorization for such properties. We tested our approach by evaluating the embeddings computed from the same Knowledge Graph through several embedding techniques with the final goal of providing insights into the choice of the most suitable technique for each context and encouraging a more conscious use of such approaches.

Deep Learning Approach for Cardiac Electrophysiology Modeling

Abstract:  
Imaging the electrical activity of the heart can be achieved with invasive catheterisation. However, the resulting data are sparse and noisy. Mathematical modelling of cardiac electrophysiology can help the analysis but solving the associated mathematical systems can become unfeasible. It is often computationally demanding, for instance when solving for different patient conditions. We present a new framework to model the dynamics of cardiac electrophysiology at lower cost. It is based on the integration of a low-fidelity physical model and a learning component implemented here via neural networks. The latter acts as a complement to the physical part, and handles all quantities and dynamics that the simplified physical model neglects. We demonstrate that this framework allows us to reproduce the complex dynamics of the transmembrane potential and to correctly identify the relevant physical parameters, even when only partial measurements are available. This combined model-based and data-driven approach could improve cardiac electrophysiological imaging and provide predictive tools.

Open discussion on the two contributions