Using metadata for content indexing within an OER network

This paper outlines the ICT solution for a metadata portal indexing open educational resources within a network of institutions. The network is aimed at blending academic and entrepreneurial knowledge,by enabling higher education institutions to publish various academic learning resources e.g. video lectures, course planning materials, or thematic content, whereasenterprises can present different forms of expert knowledge, such as case studies, expert presentations on specific topics, demonstrations of software implementation in practice and the like. As these resources need to bediscoverable, accessible and shared by potential learners across the learning environment, it is very important that they are well described and tagged in a standard way in machine readable form by metadata. Only then can they be successfully used and reused, especially when a large amount of these resources is reached, which makes it hard for the user to locate efficiently those of interest. The metadata set adopted in our approach relies on two standards: Dublin Core and Learning Object Metadata. The aim of metadata and the corresponding metadata portal described in this paper is to provide structured access to information on open educational resources within the network

A multilingual evaluation dataset for monolingual word sense alignment

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.The authors would like to thank the three anonymous reviewers for their insightful suggestions and careful reading of the manuscript. This work has received funding from the EU’s Horizon 2020 Research and Innovation programme through the ELEXIS project under grant agreement No. 731015. The contributions in Bulgarian were partially funded by the Bulgarian National Interdisciplinary Research e-Infrastructure for Resources and Technologies in favor of the Bulgarian Language and Cultural Heritage, part of the EU infrastructures CLARIN and DARIAH – CLaDA-BG, Grant number DO1- 272/16.12.2019. This work is also supported by Sci- ence Foundation Ireland (SFI) under the Insight Center for Data Analytics (Grant Number SFI/12/RC/2289) and the Irish Research Council under the “Cardamom” Consolidator Laureate Grant (IRCLA/2017/129).peer-reviewed2020-05-1