RDF graphs comprise highly complex data, both from a structural and from a semantic perspective. This makes them hard to discover and learn, and hinders their usability.
An elegant basis for summarizing graphs is provided by the graph quotient formalism. In a nutshell, a graph quotient specifies a way to view some graph nodes as equivalent to each other, and represents a graph through its equivalence classes based on this equivalence.
I will present work carried in my last team over the last few years, on quotient summarization of semantic-rich RDF graph. In particular, I will introduce a set of summaries particularly suited for the heterogeneous structure of RDF graphs, and discuss novel results at the interplay of summarization and saturation with RDF Schema rules.
Ioana Manolescu is the lead of the CEDAR Inria team, focusing on rich data analytics at cloud scale. She is a member of the PVLDB Endowment Board of Trustees, and a co-president of the ACM SIGMOD Jim Gray PhD dissertation committee. Recently, she has been a general chair of the IEEE ICDE 2018 conference, an associate editor for PVLDB 2017 and 2018, and the program chair of SSDBBM 2016. She has co-authored more than 130 articles in international journals and conferences, and contributed to several books. Her main research interests include data models and algorithms for computational fact-checking, performance optimizations for semistructured data and the Semantic Web, and distributed architectures for complex large data.
