Milan Dojchinovski


2022

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A Survey of Guidelines and Best Practices for the Generation, Interlinking, Publication, and Validation of Linguistic Linked Data
Fahad Khan | Christian Chiarcos | Thierry Declerck | Maria Pia Di Buono | Milan Dojchinovski | Jorge Gracia | Giedre Valunaite Oleskeviciene | Daniela Gifu
Proceedings of the 8th Workshop on Linked Data in Linguistics within the 13th Language Resources and Evaluation Conference

This article discusses a survey carried out within the NexusLinguarum COST Action which aimed to give an overview of existing guidelines (GLs) and best practices (BPs) in linguistic linked data. In particular it focused on four core tasks in the production/publication of linked data: generation, interlinking, publication, and validation. We discuss the importance of GLs and BPs for LLD before describing the survey and its results in full. Finally we offer a number of directions for future work in order to address the findings of the survey.

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Cross-Lingual Link Discovery for Under-Resourced Languages
Michael Rosner | Sina Ahmadi | Elena-Simona Apostol | Julia Bosque-Gil | Christian Chiarcos | Milan Dojchinovski | Katerina Gkirtzou | Jorge Gracia | Dagmar Gromann | Chaya Liebeskind | Giedrė Valūnaitė Oleškevičienė | Gilles Sérasset | Ciprian-Octavian Truică
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this paper, we provide an overview of current technologies for cross-lingual link discovery, and we discuss challenges, experiences and prospects of their application to under-resourced languages. We rst introduce the goals of cross-lingual linking and associated technologies, and in particular, the role that the Linked Data paradigm (Bizer et al., 2011) applied to language data can play in this context. We de ne under-resourced languages with a speci c focus on languages actively used on the internet, i.e., languages with a digitally versatile speaker community, but limited support in terms of language technology. We argue that languages for which considerable amounts of textual data and (at least) a bilingual word list are available, techniques for cross-lingual linking can be readily applied, and that these enable the implementation of downstream applications for under-resourced languages via the localisation and adaptation of existing technologies and resources.

2016

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Crowdsourced Corpus with Entity Salience Annotations
Milan Dojchinovski | Dinesh Reddy | Tomáš Kliegr | Tomáš Vitvar | Harald Sack
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we present a crowdsourced dataset which adds entity salience (importance) annotations to the Reuters-128 dataset, which is subset of Reuters-21578. The dataset is distributed under a free license and publish in the NLP Interchange Format, which fosters interoperability and re-use. We show the potential of the dataset on the task of learning an entity salience classifier and report on the results from several experiments.

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DBpedia Abstracts: A Large-Scale, Open, Multilingual NLP Training Corpus
Martin Brümmer | Milan Dojchinovski | Sebastian Hellmann
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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.

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FREME: Multilingual Semantic Enrichment with Linked Data and Language Technologies
Milan Dojchinovski | Felix Sasaki | Tatjana Gornostaja | Sebastian Hellmann | Erik Mannens | Frank Salliau | Michele Osella | Phil Ritchie | Giannis Stoitsis | Kevin Koidl | Markus Ackermann | Nilesh Chakraborty
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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.