Reconciling Heterogeneous Descriptions of Language Resources

Abstract

Language resources are a cornerstone of linguistic research and for the development of natural language processing tools, but the discovery of relevant resources remains a challenging task. This is due to the fact that relevant metadata records are spread among different repositories and it is currently impossible to query all these repositories in an integrated fashion, as they use different data models and vocabularies. In this paper we present a first attempt to collect and harmonize the metadata of different repositories, thus making them queriable and browsable in an integrated way. We make use of RDF and linked data technologies for this and provide a first level of harmonization of the vocabularies used in the different resources by mapping them to standard RDF vocabularies including Dublin Core and DCAT. Further, we present an approach that relies on NLP and in particular word sense disambiguation techniques to harmonize resources by mapping values of attributes-such as the type, license or intended use of a resource-into normalized values. Finally, as there are duplicate entries within the same repository as well as across different repositories, we also report results of detection of these duplicates.

Multilinguality and LLOD: A survey across linguistic description levels

“…The other method is a collaborative approach, for example, the LRE Map 32 or DataHub.io, 33 which allow anyone to publish language resource metadata increasing the coverage but decreasing the control over the quality. An approach to reconcile linguistic resource metadata from all these repositories as linked data in a single interface has been presented in the form of LingHub 34 [167,168]. [54] present the development of the Ontologies of Linguistic Annotation (OLiA) [36] since 2006, which provide comprehensive annotation terminology for linguistic phenomena.…”

“…For instance, NexusLinguarum 36 is organising a series of training schools around the topic of linguistic linked data, and has supported a number of tutorials and seminars on this topic. Additionally, Linghub, developed in the context of the LIDER 37 and Prêt-à-LLOD 38 projects, aims at alleviating the issue of discoverability and reusability of language resources [168], by indexing a large amount of language resources metadata in a way that can be easily exploited by software agents as well as by humans.…”

When linguistics meets web technologies. Recent advances in modelling linguistic linked data

“…The NLP Interchange Format (NIF), 123 developed at AKSW Leipzig, was designed to facilitate the integration of NLP tools in knowledge extraction pipelines, as part of the building of a Semantic Web tool chain and a technology stack for language technology on the web [86]. NIF provides support for a broad range of frequently occurring NLP tasks such as part of speech tagging, lemmatization, entity linking, coreference resolution, sentiment analysis, and, to a limited extent, syntactic and semantic parsing.…”

“…They are deployed in the description of language resources in catalogues of language resources. More specifically, the first version of MS-OWL is used in LingHub, 153 a data portal aggregating metadata records for language resources hosted in various repositories and catalogues [122,123], while the second version, the one described here, is used in the European Language Grid, 154 which is a platform for language resources with a focus on industry-relevant Language Technology in Europe [145]. Amongst the immediate plans, crosswalks with DCAT and schema.org are a priority, to ensure wider uptake and interoperability with (meta)data from other communities.…”