Modeling, Generating and Publishing knowledge as Linked Data tutorial @EKAW2016

Knowledge Acquisition and Modeling are important in a world with large heterogenous data sources. This process of extracting, structuring, and organizing knowledge from one or multiple data sources is required to construct knowledge-intensive systems and services for the Semantic Web. This way, the processing of large and originally semantically heterogeneous data sources is enabled and new knowledge is captured. Thus, offering existing data as Linked Data increases its shareability, extensibility and reusability. However, using Linking Data, as a means to represent knowledge, has proven to be easier said than done. During this tutorial, we will elaborate the importance of semantically annotating data and how existing technologies facilitate their mapping to Linked Data. We will introduce the [R2]RML, language(s) to generate Linked Data derived from different heterogeneous data sources, e.g., tabular data in databases, data in XML published as Open Data or data in JSON derived from a Web API. More, we will 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 to the end users. Last, we will show how to easily publish Linked Data with LDF. In the end, participants, independently of their knowledge background, will have model, annotate and publish some data on their own!


The goal of this tutorial is to show that domain-experts can model the knowledge as Linked Data without being aware of Semantic Web technologies or being dependent on Semantic Web experts. By the end of this tutorial, knowledge management or domain experts, as well as data specialists and publishers should know how to profit of modeling the knowledge that appears in their data as Linked Data, as well as how to annotating their data to generate and publish them as Linked Data, and getting a chance to have some practical experience. The tutorial aims to show that non-Semantic Web experts can easily model the knowledge, that exists in data and thus, that Linked Data generation and publication is made easy.

This tutorial touches the following conference topics:

  • Knowledge in evolving and local contexts
    • Methods and methodologies for context awareness
      • Modelling of contextualised knowledge
      • Ontology design patterns for representing context
    • Lessons learned from case studies
      • Adoption of semantic web technologies
  • Knowledge Management
    • Methodologies and tools for knowledge management
    • Knowledge sharing and distribution, collaboration
    • Provenance and trust in knowledge management
    • Web 2.0 technologies for knowledge management
  • Knowledge Engineering and Acquisition
    • Tools and methodologies for ontology engineering
    • Knowledge authoring and semantic annotation
    • Knowledge acquisition from non-ontological resources (thesauri, folksonomies etc.)
    • Uncertainty and vagueness in knowledge representation


Morning Session
09:00 - 10:30
Afternoon Session
RML tools in practice


This tutorial refers to knowledge/data experts with first learning about Linked Data acquisition and generation, as well as those who already have some background in them. It will assume only background knowledge of the basics of knowledge acquisition/representation/management, Linked Data and the Semantic Web. Helpful, but not required, are the basics of RDF and OWL.


It is expected participants who are knowledge management or data experts who are interested in using and profiting of Semantic Web technologies but still face the barriers of modeling their data as Linked Data.


Anastasia Dimou
Ghent University - imec - @natadimou
Anastasia Dimou is a Scientific Researcher at Ghent University - iMinds since February 2013. 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.

Pieter Heyvaert
Ghent University - imec - @HeyPieter
Pieter Heyvaert is a researcher at Ghent University - iMinds 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.

Ruben Verborgh
Ghent University - imec - @RubenVerborgh
Ruben Verborgh is a researcher in semantic hypermedia at Ghent University – iMinds, Belgium and a postdoctoral fellow of the Research Foundation Flanders. He explores the connection between Semantic Web technologies and the Web's architectural properties, with the ultimate goal of building more intelligent clients. Along the way, he became fascinated by Linked Data, REST/hypermedia, Web APIs, and related technologies. He is a co-author of two books on Linked Data, and has contributed to more than 150 publications for international conferences and journals on Web-related topics.

Ruben Taelman
Ghent University - imec - @rubensworks
Ruben Taelman is researcher and PhD student at Ghent University - imec since August 2015. He investigates methods for publishing, storing and querying Linked Data, with a focus on dynamic data domains such as versioning and stream processing.