Recent developments for the linguistic linked open data infrastructure

In this paper we describe the contributions made by the European H2020 project “Pret-a-LLOD” (‘Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors’) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Pret-a-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards

Results of the Translation Inference Across Dictionaries 2021 Shared Task

The objective of the Translation Inference Across Dictionaries (TIAD) shared task is to explore and compare methods and techniques that infer translations indirectly between language pairs, based on other bilingual/multilingual lexicographic resources. In this forth edition the participating systems were asked to generate new translations automatically among three languages - English, French, Portuguese - based on known indirect translations contained in the Apertium RDF graph. Such evaluation pairs have been the same during the three last TIAD editions. The main novelty this time has been the use of a larger graph as a basis to produce the translations, which is the Apertium RDF v2, and the introduction of improved evaluation metrics. The evaluation of the results was carried out by the organisers against manually compiled language pairs of K Dictionaries. For the first time in the TIAD series, some systems beat the proposed baselines. This paper gives an overall description of the shard task, the evaluation data and methodology, and the systems’ results

Bilingual dictionary generation and enrichment via graph exploration

In recent years, we have witnessed a steady growth of linguistic information represented and exposed as linked data on the Web. Such linguistic linked data have stimulated the development and use of openly available linguistic knowledge graphs, as is the case with the Apertium RDF, a collection of interconnected bilingual dictionaries represented and accessible through Semantic Web standards. In this work, we explore techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations). We build upon a cycle density based method: partitioning the graph into biconnected components for a speed-up, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements. We also analyse the shortcomings of traditional evaluation metrics used for translation inference and propose to complement them with new ones, both-word precision (BWP) and both-word recall (BWR), aimed at being more informative of algorithmic improvements. Over twenty-seven language pairs, our algorithm produces dictionaries about 70% the size of existing Apertium RDF dictionaries at a high BWP of 85% from scratch within a minute. Human evaluation shows that 78% of the additional translations generated for dictionary enrichment are correct as well. We further describe an interesting use-case: inferring synonyms within a single language, on which our initial human-based evaluation shows an average accuracy of 84%. We release our tool as free/open-source software which can not only be applied to RDF data and Apertium dictionaries, but is also easily usable for other formats and communities.This work was partially funded by the Prêt-à-LLOD project within the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 825182. This article is also based upon work from COST Action CA18209 NexusLinguarum, “European network for Web-centred linguistic data science”, supported by COST (European Cooperation in Science and Technology). It has been also partially supported by the Spanish projects TIN2016-78011-C4-3-R and PID2020-113903RB-I00 (AEI/FEDER, UE), by DGA/FEDER, and by the Agencia Estatal de Investigación of the Spanish Ministry of Economy and Competitiveness and the European Social Fund through the “Ramón y Cajal” program (RYC2019-028112-I)

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

This article provides an up-to-date and comprehensive survey of models (including vocabularies, taxonomies and ontologies) used for representing linguistic linked data (LLD). It focuses on the latest developments in the area and both builds upon and complements previous works covering similar territory. The article begins with an overview of recent trends which have had an impact on linked data models and vocabularies, such as the growing influence of the FAIR guidelines, the funding of several major projects in which LLD is a key component, and the increasing importance of the relationship of the digital humanities with LLD. Next, we give an overview of some of the most well known vocabularies and models in LLD. After this we look at some of the latest developments in community standards and initiatives such as OntoLex-Lemon as well as recent work which has been in carried out in corpora and annotation and LLD including a discussion of the LLD metadata vocabularies META-SHARE and lime and language identifiers. In the following part of the paper we look at work which has been realised in a number of recent projects and which has a significant impact on LLD vocabularies and models

Inducing the Cross-Disciplinary Usage of Morphological Language Data Through Semantic Modelling

Despite the enormous technological advancements in the area of data creation and management the vast majority of language data still exists as digital single-use artefacts that are inaccessible for further research efforts. At the same time the advent of digitisation in science increased the possibilities for knowledge acquisition through the computational application of linguistic information for various disciplines. The purpose of this thesis, therefore, is to create the preconditions that enable the cross-disciplinary usage of morphological language data as a sub-area of linguistic data in order to induce a shared reusability for every research area that relies on such data. This involves the provision of morphological data on the Web under an open license and needs to take the prevalent diversity of data compilation into account. Various representation standards emerged across single disciplines which lead to heterogeneous data that differs with regard to complexity, scope and data formats. This situation requires a unifying foundation enabling direct reusability. As a solution to fill the gap of missing open data and to overcome the presence of isolated datasets a semantic data modelling approach is applied. Being rooted in the Linked Open Data (LOD) paradigm it pursues the creation of data as uniquely identifiable resources that are realised as URIs, accessible on the Web, available under an open license, interlinked with other resources, and adhere to Linked Data representation standards such as the RDF format. Each resource then contributes to the LOD cloud in which they are all interconnected. This unification results from ontologically shared bases that formally define the classification of resources and their relation to other resources in a semantically interoperable manner. Subsequently, the possibility of creating semantically structured data has sparked the formation of the Linguistic Linked Open Data (LLOD) research community and LOD sub-cloud containing primarily language resources. Over the last decade, ontologies emerged mainly for the domain of lexical language data which lead to a significant increase in Linked Data-based linguistic datasets. However, an equivalent model for morphological data is still missing, leading to a lack of this type of language data within the LLOD cloud. This thesis presents six publications that are concerned with the peculiarities of morphological data and the exploration of their semantic representation as an enabler of cross-disciplinary reuse. The Multilingual Morpheme Ontology (MMoOn Core) as well as an architectural framework for morphemic dataset creation as RDF resources are proposed as the first comprehensive domain representation model adhering to the LOD paradigm. It will be shown that MMoOn Core permits the joint representation of heterogeneous data sources such as interlinear glossed texts, inflection tables, the outputs of morphological analysers, lists of morphemic glosses or word-formation rules which are all equally labelled as “morphological data” across different research areas. Evidence for the applicability and adequacy of the semantic modelling entailed by the MMoOn Core ontology is provided by two datasets that were transformed from tabular data into RDF: the Hebrew Morpheme Inventory and Xhosa RDF dataset. Both further demonstrate how their integration into the LLOD cloud - by interlinking them with external language resources - yields insights that could not be obtained from the initial source data. Altogether the research conducted in this thesis establishes the foundation for an interoperable data exchange and the enrichment of morphological language data. It strives to achieve the broader goal of advancing language data-driven research by overcoming data barriers and discipline boundaries