3rd Summer Datathon on
Linguistic Linked Open Data
(Dagstuhl Summer School)

  • Date
  • 12.-17.5.2019
  • Location
  • Schloss Dagstuhl
    Leibniz Center for Informatics

ELEXIS supported the Language, Data and Knowledge 2019 conference which took place in Leipzig, Germany.

The 3rd Summer Datathon on Linguistic Linked Open Data (SD-LLOD-19) was organized in conjunction with and held before the 2nd Conference on Language, Data and Knowledge (LDK-2019).


The SD-LLOD datathon has the main goal of giving people from industry and academia practical knowledge in the field of Linked Data and its application to natural language data and natural language annotations, from areas as diverse as knowledge engineering, lexicography, the language sciences, natural language processing and computational philology.

There will be tutorials and supervision provided by leading experts on Linked Open
Data and its application to language resources, human language technology and the language sciences.
The final aim is to enable participants to migrate their own linguistic data and publish them as Linked Data on the Web.


The SD-LLOD datathon is unique in its specialization worldwide and continues a series of international hackathons and summer schools organized since 2012.

This edition is supported by several international projects addressing different aspects of natural language processing, lexicography and digital humanities:
The Research Group “Linked Open Dictionaries (LiODi)”, funded by the German Federal Ministry of Education and Research (BMBF), the Horizon 2020 Research and Innovation Action “Pret-a-LLOD. Ready-to-use Multilingual Linked Language Data for Knowledge
Services across Sectors” and the Horizon 2020 Research and Innovation Action “ELEXIS. European Lexicographic Infrastructure”.


During the datathon, participants will:
– Generate and publish their own Linguistic Linked (Open) Data from some existing data sources or existing tools.
– Apply Linked Data principles and Semantic Web technologies (Ontologies, RDF, Linked Data) to language resources and human language technology.
– Use the principal models for representing Linguistic Linked Open Data, in particular Ontolex-Lemon, Web Annotation and knowledge representation vocabularies such as SKOS and OWL.
– Gather experiences with terminology resources developed for or used in the Linguistic Linked Open Data context, such as lexvo, lexinfo, OLiA and
– Learn about multilingual dataset linking against knowledge bases from the Web of Data, such as DBpedia or BabelNet.
– Learn about benefits and applications of linguistic linked data for specific use cases.

The program of the summer datathon will contain three types of sessions:
– Seminars: to show novel aspects and discuss selected topics.
– Practical sessions: to introduce the basic foundations of each topic,
methods, and technologies and where participants will perform different
tasks using the methods and technologies presented.
– Hacking sessions: participants will follow the whole process of generating and publishing Linguistic Linked Data with some existing data

In addition to  participation, attendants are invited to propose a  miniproject related to the topic and to bring to the datathon some dataset of linguistic data produced by their organizations  to work with during the hacking sessions and transform into linked data. Participants who cannot provide their own linguistic dataset can join another’s miniproject
or some of the ones proposed by the organisers.
There will be an award to  the best miniproject.

Participants should bring their own laptops to follow and participate in  the hacking sessions, they will be provided with digital copies of all  necessary material used during the course and will have assistance for installing all the required software.

Read about the experience and impressions of Sina Ahmadi, ELEXIS researcher and Tutor at the 3rd Summer Datathon on Linguistic Linked Open Data in his blog post (feat. Bach’s Minuet in G major):


Gerard de Melo,
Director of the Deep Data Lab,
Department of Computer Science,
Rutgers University,
New Jersey

Richard Eckart de Castilho,
Ubiquitous Knowledge Processing (UKP) Lab,
Department of Computer Science,
Technische Universität Darmstadt,


Christian Chiarcos (Goethe Universität Frankfurt, Germany)
Jorge Gracia (University of Zaragoza, Spain)
John P. McCrae (Insight Centre for Data Analytics, NUI Galway, Ireland)