Building a Linked Open Data Cloud of Linguistic Resources: Motivations and Developments

Abstract

We describe on going community-efforts to create a Linked Open Data (sub-)cloud of linguistic resources, with an emphasis on resources that are specific to linguistic research, namely annotated corpora and linguistic databases. We argue that for both types of resources, the application of the Linked Open Data paradigm and the representation in RDF represents a promising approach to address interoperability problems, and to integrate information from different repositories. This is illustrated with example studies for different kinds of linguistic resources. The efforts described in this chapter are conducted in the context of the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation. The OWLG is a network of researchers interested in linguistic resources and/or their publication under open licenses, and a number of its members are engaged in the application of the Linked Open Data paradigm to their resources. Under the umbrella of the OWLG, these efforts will eventually emerge in the creation of a Linguistic Linked Open Data cloud (LLOD). I. Gurevych and J. Kim (eds.), The People's Web Meets NLP, Theory and Applications of Natural Language Processing, DOI 10.1007/978-3-642-35085-6 12, © Springer-Verlag Berlin Heidelberg 2013 315 316 C. Chiarcos et al. 12.1 Background and Motivation In recent years, the limited interoperability between linguistic resources has been recognized as a major obstacle for data use and re-use within and across discipline boundaries. After half a century of computational linguistics [24], quantitative typology [33], empirical, corpus-based study of language [29], and computational lexicography [63], researchers in Computational Linguistics, Natural Language Processing (NLP) or Information Technology, as well as in Digital Humanities, are confronted with an immense wealth of linguistic resources, that are not only growing in number, but also in their heterogeneity. Interoperability involves two aspects [40]: Structural ('syntactic') interoperability: Resources use comparable formalisms to represent and to access data (formats, protocols, query languages, etc.), so that they can be accessed in a uniform way and that their information can be integrated with each other. Conceptual ('semantic') interoperability: Resources share a common vocabu-lary, so that linguistic information from one resource can be resolved against information from another resource, e.g., grammatical descriptions can be linked to a terminology repository.