http://www.inf.unibz.it/~calvanese/teaching/2020-01-BigDat/

Virtual Knowledge Graphs for Data Integration

Course at the
6th International Winter School on Big Data (BigDat 2020)
Ancona, Italy, 13-17 January 2020

Research Centre for Knowledge and Data (KRDB)
Free University of Bozen-Bolzano

Department of Computing Science
Umeå University, Sweden

Slides of the course

Summary

Recently, semantic technologies have been successfully deployed to overcome the typical difficulties in accessing and integrating data stored in different kinds of legacy sources. In particular, knowledge graphs are being used as a mechanism to provide a uniform representation of heterogeneous information. In such graphs, data are represented in the RDF format, and are complemented by an ontology that can be queried using the standard SPARQL language. The RDF graph is often obtained by materializing source data, following the traditional extract-transform-load workflow. Alternatively, the sources are declaratively mapped to the ontology, and the RDF graph is maintained virtual. In such an approach, usually called Virtual Knowledge Graphs (VKG), query answering is based on sophisticated query transformation techniques. In this tutorial:

we provide a general introduction to relevant semantic technologies;
we illustrate the principles underlying the VKG approach to data integration, providing insights into its theoretical foundations, and describing well-established algorithms, techniques, and tools;
we discuss relevant use-cases using VKGs;
we provide a hands-on experience with the stat-of-the-art VKG system Ontop.

Syllabus

Motivation
Virtual Knowledge Graphs for Data Access
VKG Framework
VKG Systems and Usecases
Query Answering over VKGs
Recent Developments and Future Plans
Conclusions
Hands-on Exercises

References

Guohui Xiao, Diego Calvanese, Roman Kontchakov, Domenico Lembo, Antonella Poggi, Riccardo Rosati, and Michael Zakharyaschev. Ontology-Based Data Access: A Survey. In: Proc. of the 27th Int. Joint Conf. on Artificial Intelligence (IJCAI). IJCAI Org., 2018, pp. 5511-5519. doi: 10.24963/ijcai.2018/777.
Guohui Xiao, Linfang Ding, Benjamin Cogrel, and Diego Calvanese. Virtual Knowledge Graphs: An Overview of Systems and Use Cases. In: Data Intelligence 1.3 (2019), pp. 201-223. doi: 10.1162/dint_a_00011.
Diego Calvanese, Benjamin Cogrel, Sarah Komla-Ebri, Roman Kontchakov, Davide Lanti, Martin Rezk, Mariano Rodriguez-Muro, and Guohui Xiao. Ontop: Answering SPARQL Queries over Relational Databases. In: Semantic Web J.8.3 (2017), pp. 471-487. doi: 10.3233/SW-160217.
Diego Calvanese, Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, Antonella Poggi, Mariano Rodriguez-Muro, and Riccardo Rosati. Ontologies and Databases: The DL-Lite Approach. In: Reasoning Web: Semantic Technologies for Informations Systems - 5th Int. Summer School Tutorial Lectures (RW). Vol. 5689. Lecture Notes in Computer Science. Springer, 2009, pp. 255-356.

Prerequisite Knowledge

Basics about relational databases, first-order logic, and data modeling, as typically taught in BSc-level Computer Science courses. A background in logics for knowledge representation, description logics, and complexity theory, might be useful to establish cross-connections, but is not required to follow the course.

Course Duration

Three lectures of 1.5 hours each.

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Last modified: Friday, 31-Jan-2020 2:41:02 CET