Jeffrey Sardina
Title: Improving usability (for developers) of the interface between knowledge graphs and machine learning
Supervision Team: Declan O’Sullivan, TCD / John Kelleher, TU Dublin / Fergal Marrinan, SONAS Innovation
Description: Knowledge Graphs (KGs) have been successfully adopted in many domains, both in academia and enterprise settings–enabling one to integrate heterogeneous data sources to facilitate research, business analytics, fraud detection, and so on. Increasingly they are being used by Machine Learning algorithms through Knowledge Graph embeddings. However few environments exist that aid practitioners in either discipline to easily interface the two technologies together, e.g. Machine Learning experts to easily explore and produce Knowledge Graph Embeddings; or Knowledge Graph engineers to easily prepare access to their knowledge graphs to suit particular machine learning algorithms. The PhD will thus identify, propose, and develop an approach for allowing machine learning experts to engage with knowledge graphs, and enable knowledge graph engineers to interface their graphs tailored to target machine learning algorithms being used. In order to provide context for the research, health domain will be used, specifically the study of Cancer, where the bringing together of Machine Learning and Knowledge Graphs is desired and already being progressed. This PhD will be aligned with the sponsorship by Sonas Innovation (http://sonasi.com) of d-real PhDs, and will also benefit from research ongoing within the SFI ADAPT Research Centre at TCD
