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Knowledge Representation as Linked Data

Full-day tutorial at CIKM 2018- Friday, 26 October 2018

Anastasia Dimou

Ghent University, Belgium

Dr Anastasia Dimou is a Senior Researcher at imec and post-doctoral researcher at Ghent University. Her research interests include Linked Data Generation and Publication, Data Quality and Integration, Knowledge Representation and Management. As part of her research, she investigated a uniform language for describing the mapping rules for generating high quality Linked Data from multiple heterogeneous data formats and access interfaces. Anastasia is also working on Linked Data generation and publishing workflows. Anastasia covers the Linked Data and Semantic Web introduction, [R2]RML based schema transformations and the rule-based Linked Data generation.

Pieter Heyvaert

Ghent University, Belgium

Pieter Heyvaert is a researcher at Ghent University – imec since October 2014. His research interests include Linked Data Generation and Publication, Knowledge Representation and Modeling. As part of his research, he aims to support non-Semantic Web experts to model knowledge as Linked Data. Pieter covers the UIs for defining rules for Linked Data generation.

Ben De Meester

Ghent University, Belgium

Ben De Meester joined the IDlab research group of Ghent University – imec, Belgium as a full-time researcher immediately after he graduated from Ghent University in 2013 as a Master of Computer Science. His research interests include Linked Data Generation and Publication, Knowledge Representation and Modeling. As part of his research, he focusses on declarative solutions for generation, transformation, and validation of Linked Data. Ben covers the data transformations and Linked Data validation.

Ruben Taelman

Ghent University, Belgium

Ruben Taelman is a PhD student at IDLab, Ghent University – imec, Belgium. His research concerns the server and client trade-offs for Linked Data publication and querying, with a particular focus on dynamic data, such as streams and versioning. Ruben covers the Linked Data publication part of the tutorial.



Abstract

Knowledge acquisition and modelling are important in a world with heterogeneous data. Extracting, processing, structuring, and organizing knowledge from one or multiple sources is required to capture knowledge, increasing its shareability, extensibility and reusability. However, using Linking Data for knowledge representation is easier said than done! During this tutorial, we elaborate on the importance of representing knowledge as Linked Data. We introduce the [R2]RML language(s) to semantically annotate raw data and generate Linked Data derived and combined from different heterogeneous data. This can be, e.g., tabular data in databases, data in XML published as Open Data or data in JSON derived from Web APIs. Moreover, we support non-Semantic Web experts to annotate their data with the RMLEditor. Through the tool’s innovative user interface all underlying Semantic Web technologies are invisible. Last, we show how to easily publish Linked Data. In the end, participants model, annotate and publish data on their own!



Detailed Outline

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Link to External Resources

Tutorial resources