| 總結: | Relational data is ubiquitous in modern-day computing, and drives several key applications across multiple domains, such as information retrieval, question answering, recommendation systems, and drug discovery. As a result, a main research question in artificial intelligence (AI) is to build models that exploit relational data in an efficient and reliable way, while injecting the relevant inductive biases and being robust to input noise. In recent years, neural models such as graph neural networks (GNNs) and shallow node embedding models have enabled significant breakthroughs in learning representations over relational structures. However, the abilities and limitations of these systems are not completely understood, and several challenges remain to endow these models with reliability guarantees, enrich their relational inductive bias, and apply them in more challenging problem settings. In this thesis, we study learning and inference over relational data. More specifically, we analyse the properties and limitations of existing models both theoretically and empirically, and propose new approaches with improved relational inductive bias and representation power.
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