Reasoning Web. Learning, Uncertainty, Streaming, and Scalability

The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shownuseful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.

Auteur

Since December 2016, Claudia d'Amato is in tenure track for associate professorship in Computer Science at the University of Bari. She obtained the Italian Habilitation for the functions of Associate Professor for the Scientific Sector "01/B1 - Informatics" on January 29th, 2014, (Application: round 2012). Before she has been appointed as assistant professor at the University of Bari on January 2016 and I have been research fellow at the University of Bari since May 2007 to December 2015. Martin Theobald (born 1976) has been appointed as a Professor of Computer Science with a focus on "Big Data" by the Faculty of Science, Technology and Communication (FSTC) at the University of Luxembourg in February 2017.

Texte du rabat

This volume contains lecture notes of the 14th Reasoning Web Summer School (RW 2018), held in Esch-sur-Alzette, Luxembourg, in September 2018.

The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.

Contenu

Practical Normative Reasoning with Defeasible Deontic Logic.- Efficient SPARQL Queries on Very Large Knowledge Graphs.- A Tutorial on Query Answering and Reasoning over Probabilistic Knowledge Bases.- Cold-start Knowledge Base Population using Ontology-based Information Extraction with Conditional Random Fields.- Machine Learning with and for Knowledge Graphs.- Rule Induction and Reasoning over Knowledge Graphs.- Storing and Querying Semantic Data in the Cloud.- Engineering of Web Stream Processing Applications.- Reasoning at Scale.