• Philipp Cimiano
  • Christian Chiarcos
  • John P. McCrae
  • Jorge Gracia


The Linguistic Linked Data (LLD) paradigm was introduced about 8 years ago by the Open Linguistics Working Group (OWLG). The original mission of this group was to (1) promote the use of open standards in linguistics; (2) act as a central point of reference and provide support for those interested in open linguistic data; (3) develop best practices and use cases concerning the creation, use and distribution of linguistic data; and (4) build and maintain an index of open linguistic data sources.

This book has provided? an overview of the main principles, methods and best practices involved in the application of linked data principles to the modelling and publication of language resources. Following these practices supports the creation of an ecosystem of linguistic linked datasets linked to each other and harmonized by using the same vocabularies that allows to query/filter the data for some phenomenon of interest in a straightforward fashion by running a query over all the datasets. Such an ecosystem of linguistic linked data also facilitates the discovery of resources and supports their automatic transformation. Bringing about these benefits requires normalization at the syntactic and semantic level. Without semantic normalization, none of the above-mentioned benefits is possible. The vocabularies, models and best practices described in this book represent a significant step further in striving for an ecosystem in which data is semantically interoperable and thus easier to reuse and query across datasets. We believe that in the data-driven society in which we live, openness and, as a direct consequence, reuse of datasets are key to creating a level playing field in which data is FAIR and can be effectively used by many. The principles described in this book have the potential to represent a game changer in the way we publish and work with data, in particular with language resources.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Semantic Computing GroupBielefeld UniversityBielefeldGermany
  2. 2.Angewandte ComputerlinguistikGoethe-UniversityFrankfurt am MainGermany
  3. 3.Insight Centre for Data AnalyticsNational University of IrelandGalwayIreland
  4. 4.Aragon Institute of Engineering Research (I3A)University of ZaragozaZaragozaSpain

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