Matteo Lissandrini will present his work on November 30th at 11AM.
It will be online at https://cnrs.zoom.us/j/98523306322?pwd=dE1pRjg2SXRSb1F1cFYvWlZGSHZmUT09
Title
Knowledge Graphs Exploration Systems: the path forward
Abstract
Knowledge graphs (KGs) represent facts in the form of nodes and relationships and are widely used to represent and share knowledge in many different domains. However, their widespread adoption to model and integrate different data sources, along with their complex generation processes, have made KGs very complicated and difficult to understand. The need to obtain a better understanding of complex KGs lead to the advent of a wide range of knowledge graph exploration approaches to better understand their contents and extract relevant insights. Nevertheless, the needs of current KG exploration use cases are not met (even neglected) by existing KG data management systems. Hence, in this talk, I will be surveying the common information needs across applications and use cases linking them to an overview of state-of-the-art approaches for KG exploration. Through this analysis, we can identify the (mostly unmet) requirements for effective KG exploration systems. Thus, I will provide an overview of some promising research directions for the realization of a system able to fully support knowledge graph exploration.
Bio
Matteo Lissandrini is an Assistant Professor in the Department of Computer Science at Aalborg University working on Data Exploration and Graph Data Management systems. He is currently researching on Graph-powered Big-Data Exploration, Semantic Data-Integration systems, and Self-Optimizing Graph Data Management Systems (Graph DBMSes). Matteo has been a Marie Skłodowska Curie IF fellow. He received his PhD from the University of Trento (Italy, 2018) with a thesis on exploratory search for information graphs. He has been visiting researcher at the HP Labs in Palo Alto, California in 2013, at the Cheriton School of Computer Science at the University of Waterloo, Canada, in 2014, and the Laboratory of Computer Science at the University of Edinburgy, U.K., in 2018.
