The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections. A major outcome of our work is the Linguistic Linked Open Data (LLOD) cloud, an LOD (sub-)cloud of linguistic resources, which covers various linguistic databases, lexicons, corpora, terminologies, and metadata repositories. We present and summarize five years of progress on the development of the cloud and of advancements in open data in linguistics, and we describe recent community activities. The paper aims to serve as a guideline to orient and involve researchers with the community and/or Linguistic Linked Open Data.

The ever increasing importance of machine learning in Natural Language Processing is accompanied by an equally increasing need in large-scale training and evaluation corpora. Due to its size, its openness and relative quality, the Wikipedia has already been a source of such data, but on a limited scale. This paper introduces the DBpedia Abstract Corpus, a large-scale, open corpus of annotated Wikipedia texts in six languages, featuring over 11 million texts and over 97 million entity links. The properties of the Wikipedia texts are being described, as well as the corpus creation process, its format and interesting use-cases, like Named Entity Linking training and evaluation.

In the recent years, Linked Data and Language Technology solutions gained popularity. Nevertheless, their coupling in real-world business is limited due to several issues. Existing products and services are developed for a particular domain, can be used only in combination with already integrated datasets or their language coverage is limited. In this paper, we present an innovative solution FREME - an open framework of e-Services for multilingual and semantic enrichment of digital content. The framework integrates six interoperable e-Services. We describe the core features of each e-Service and illustrate their usage in the context of four business cases: i) authoring and publishing; ii) translation and localisation; iii) cross-lingual access to data; and iv) personalised Web content recommendations. Business cases drive the design and development of the framework.

As language resources start to become available in linked data formats, it becomes relevant to consider how linked data interoperability can play a role in active language processing workflows as well as for more static language resource publishing. This paper proposes that linked data may have a valuable role to play in tracking the use and generation of language resources in such workflows in order to assess and improve the performance of the language technologies that use the resources, based on feedback from the human involvement typically required within such processes. We refer to this as Active Curation of the language resources, since it is performed systematically over language processing workflows to continuously improve the quality of the resource in specific applications, rather than via dedicated curation steps. We use modern localisation workflows, i.e. assisted by machine translation and text analytics services, to explain how linked data can support such active curation. By referencing how a suitable linked data vocabulary can be assembled by combining existing linked data vocabularies and meta-data from other multilingual content processing annotations and tool exchange standards we aim to demonstrate the relative ease with which active curation can be deployed more broadly.

In the last couple of years the amount of structured open government data has increased significantly. Already now, citizens are able to leverage the advantages of open data through increased transparency and better opportunities to take part in governmental decision making processes. Our approach increases the interoperability of existing but distributed open governmental datasets by converting them to the RDF-based NLP Interchange Format (NIF). Furthermore, we integrate the converted data into a geodata store and present a user interface for querying this data via a keyword-based search. The language resource generated in this project is publicly available for download and also via a dedicated SPARQL endpoint.

Extracting Linked Data following the Semantic Web principle from unstructured sources has become a key challenge for scientific research. Named Entity Recognition and Disambiguation are two basic operations in this extraction process. One step towards the realization of the Semantic Web vision and the development of highly accurate tools is the availability of data for validating the quality of processes for Named Entity Recognition and Disambiguation as well as for algorithm tuning. This article presents three novel, manually curated and annotated corpora (N3). All of them are based on a free license and stored in the NLP Interchange Format to leverage the Linked Data character of our datasets.

This paper describes the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN). The OWLG is an initiative concerned with linguistic data by scholars from diverse fields, including linguistics, NLP, and information science. The primary goal of the working group is to promote the idea of open linguistic resources, to develop means for their representation and to encourage the exchange of ideas across different disciplines. This paper summarizes the progress of the working group, goals that have been identified, problems that we are going to address, and recent activities and ongoing developments. Here, we put particular emphasis on the development of a Linked Open Data (sub-)cloud of linguistic resources that is currently being pursued by several OWLG members.