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!

Motivation

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

Program

Morning Session