Exploration is one of the primordial ways to accrue knowledge about the world and its nature. As we accumulate, mostly automatically, data at unprecedented volumes and speed, our datasets have become complex and hard to understand. In this context, exploratory methods offer the capabilities of progressively gathering the necessary knowledge when dealing with datasets that are to us "terra incognita". Yet, when dealing with complex data, we are also in need of powerful data models that give us the necessary expressivity to properly handle the richness and intricacies of the data at hand. Knowledge graphs (KGs) are quickly becoming the best model in this case. KGs represent facts in the form of nodes and relationships and are widely used to represent and share knowledge in many different companies and domains. The widespread adoption of knowledge graphs led to the advent of new knowledge graph exploration approaches to better understand their contents and extract relevant insights. By exploiting knowledge graphs, we can also explore a diverse set of data represented by other unstructured and semi-structured data models as well. This talk will provide an overview of exploratory methods focusing especially on the exploratory techniques for rich knowledge graphs, the advantages they offer, as well as on the abundant research opportunities in applying these methods across different domains.
Bio: Matteo Lissandrini is an Assistant Professor in the Department of Computer Science at Aalborg University working on Data Exploration and Knowledge Graph Management systems. Matteo has been a Marie Skłodowska Curie IF fellow. He received his PhD from the University of Trento (Italy) with a thesis on exploratory search for information graphs. He was also a member of the DbTrento research group. Matteo is currently researching on Exploratory Analytics on Knowledge Graphs and Graph Data Management Systems (Graph DBMSes). He has been researching on Exploratory Methods for Data Analytics, and in particular on Exemplar Queries.
