Language resources are a key component of natural language processing and related research and applications. Users of language resources have different needs in terms of format, language, topics, etc. for the data they need to use. Linghub (McCrae and Cimiano, 2015) was first developed for this purpose, using the capabilities of linked data to represent metadata, and tackling the heterogeneous metadata issue. Linghub aimed at helping language resources and technology users to easily find and retrieve relevant data, and identify important information on access, topics, etc. This work describes a rejuvenation and modernisation of the 2015 platform into using a popular open source data management system, DSpace, as foundation. The new platform, Linghub2, contains updated and extended resources, more languages offered, and continues the work towards homogenisation of metadata through conversions, through linkage to standardisation strategies and community groups, such as the Open Digital Rights Language (ODRL) community group.

Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based on a portfolio of Natural Language Processing and Content Curation services as well as a Multilingual Legal Knowledge Graph that contains semantic information and meaningful references to legal documents. We also describe different use cases with which we experiment and develop prototypical solutions.

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER.

This paper reports our experience when integrating differ resources and services into a grid environment. The use case we address implies the deployment of several NLP applications as web services. The ultimate objective of this task was to create a scenario where researchers have access to a variety of services they can operate. These services should be easy to invoke and able to interoperate between one another. We essentially describe the interoperability problems we faced, which involve metadata interoperability, data interoperability and service interoperability. We devote special attention to service interoperability and explore the possibility to define common interfaces and semantic description of services. While the web services paradigm suits the integration of different services very well, this requires mutual understanding and the accommodation to common interfaces that not only provide technical solution but also ease the user‟s work. Defining common interfaces benefits interoperability but requires the agreement about operations and the set of inputs/outputs. Semantic annotation allows defining some sort of taxonomy that organizes and collects the set of admissible operations and types input/output parameters.