Knowledge Graphs: Research Directions
Knowledge graphs are used to collect, represent and expose knowledge through a graph abstraction. This core idea is not new, having been explored for decades in works relating to graph databases, ontologies, data integration, graph algorithms, information extraction, and more besides. However, knowledge graphs as a research topic is increasingly becoming a confluence of these related areas, further attracting attention from new areas, in particular machine learning. From this confluence emerge a variety of open questions relating to how techniques traditionally addressed by different communities can be combined. In this talk, we will first look at how knowledge graphs are being used in practice, and at why they are receiving so much attention. We will discuss various graph models that can be used to represent them and query languages used to interrogate them. We will discuss the importance of schemata for imposing structure on knowledge graphs, and of ontologies and rules for defining their semantics. We will then look at graph analysis frameworks, before turning to novel machine learning techniques such as graph embeddings, graph neural networks and inductive logic programming. Throughout we will highlight some open research questions relating to how these diverse techniques can be combined.
Aidan Hogan is an Associate Professor in the Department of Computer Science (DCC) at the Universidad de Chile, and an Associate Researcher of the Millennium Institute for Foundational Research on Data (IMFD). He received his PhD in 2011 from the Digital Enterprise Research Institute (DERI) (now called INSIGHT) based in the National University of Ireland, Galway. His general research interests centre around the Semantic Web.