Common European Data Spaces (CEDS) are aimed at creating a single market for data across the EU that will power AI innovation. CEDS cover 14 sectors/domains and will allow secure, trustworthy data/AI models exchange between companies, public administrations etc. The Common European Language Data Space (LDS) is part of CEDS and is already made available in beta phase. The paper presents its technical design and implementation, its governance framework as well as use cases that demonstrate its value. LDS aspires to become part of the future European Language Technology ecosystem.

In this paper we give an overview of the approach adopted to add a layer of semantic information to the Greek Dependency Treebank [GDT]. Our ultimate goal is to come up with a large corpus, reliably annotated with rich semantic structures. To this end, a corpus has been compiled encompassing various data sources and domains. This collection has been preprocessed, annotated and validated on the basis of dependency representation. Taking into account multi-layered annotation schemes designed to provide deeper representations of structure and meaning, we describe the methodology followed as regards the semantic layer, we report on the annotation process and the problems faced and we conclude with comments on future work and exploitation of the resulting resource.