Semantic Technologies for Data Access and Integration
Full-day tutorial
Time: Friday, 26 October 2018, 08:15AM-09:45AM & 10:15AM-11:45AM & 01:00PM-02:30PMRoom: Parigi
Diego Calvanese
Free University of Bozen-Bolzano, Italy
Diego Calvanese is a full professor at the KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Italy. His research interests include formalisms for knowledge representation and reasoning, ontology languages, description logics, conceptual data modeling, data integration, graph data management, data-aware process verification, and service modeling and synthesis. He is the author of more than 350 refereed publications, including ones in the most prestigious international journals and conferences in AI and Databases, and he is one of the editors of the Description Logic Handbook. He is an associated editor of AIJ and JAIR, and member of the editorial board of JAR. He has been program chair of PODS’15 and general chair of ESSLLI’16. He is an EurAI Fellow since 2015. He has extensive experience in tutorials and keynotes, including tutorials at ISWC’07, ISWC’08, RW’09, ESSLLI’10, BigDat’15, AMW’16, EKAW’16, BigDat’17, ESSLLI’17, and BPM’17, and keynotes at BDA’12, JELIA’14, AIMSA’14, PODS’15, DL’16, and AMW’16.
Guohui Xiao
Free University of Bozen-Bolzano, Italy
Guohui Xiao is an assistant professor at the KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Italy. He is currently leading the development within the Ontop team. His main research interests are knowledge representation, description logics, semantic web, database theory, ontology-based data access, logic programming, and optimization and implementation of reasoning engines. His research has resulted in more than 60 refereed publications, many of which were published in prestigious venues like AAAI, IJCAI, KR, ISWC, EDBT, ECAI, CIKM, J. of AI Research, J. of Web Semantics, Semantic Web J., and J. of Approximate reasoning. He has been a PC co-chair of the 39th COMPSAC Symposium on Web Technologies & Data Analytics (WEDA’15). Guohui has taught several bachelor and master courses including java programming, data structure and algorithms, internet technologies, semantic web technologies, and ontology and database systems (ODBS). He has given tutorials about OBDA at the Summer School of the Chinese Semantic Web Conference 2014 and 2017, at the International Semantic Web Conference 2015, at Sun Yat-sen University (2015), at the Stanford Center for Biomedical Informatics Research (2016), and at Tianjin University (2016), and will present a tutorial at the International Joint Conference on Artificial Intelligence 2018.
Abstract
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. Such graphs represent data in the RDF format, which is complemented by an ontology and 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 ontology-based data access/integration (OBDA/I), query answering is based on sophisticated query transformation techniques. In this tutorial: (i) we provide a general introduction to semantic technologies; (ii) we illustrate the principles underlying OBDA/I, providing insights into its theoretical foundations, and describing well-established algorithms, techniques, and tools; (iii) we discuss relevant use-cases for OBDA/I; (iv) we provide an overview on some recent advancements.
Detailed Outline
SPARQL query language and SPARQL entailment regime
R2RML mapping language
The architecture of a typical OBDA/I system
The OBDA/I ecosystem
Significant use-cases
Deployment of SPARQL endpoint
Novel optimization techniques for OBDA
Optimization techniques
Integrating heterogeneous data sources through a federation layer
Integrating cross-linked datasets
Spatial and temporal data