From Linguistic Linked Data to Big Data

Dimitar Trajanov (1) , Elena-Simona Apostol (2, 3) , Radovan Garabík (4) , Katerina Gkirtzou (5) , Dagmar Gromann (6) , Chaya Liebeskind (7) , Cosimo Palma (8) , Michael Rosner (9) , Alexia Sampri (10) , Gilles Serasset (11) , Blerina Spahiu (12) , Ciprian-Octavian Truica (2) , Giedrė Valūnaitė Oleškevičienė (13)

1 UKIM - Ss. Cyril and Methodius University in Skopje
2 UPB - University Politehnica of Bucharest [Romania]
3 DIT-UPPSALA - Department of Information Technology
4 SAS - Slovak Academy of Sciences
5 ILSP - Institute for Language and Speech Processing
6 University of Vienna [Vienna]
7 JCT - Jerusalem College of Technology
8 UniOr - Università di Napoli L'Orientale = University of Naples
9 University of Malta [Malta]
10 CAM - University of Cambridge [UK]
11 GETALP - Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole
12 UNIMIB - Università degli Studi di Milano-Bicocca = University of Milano-Bicocca
13 Mykolas Romeris University

With advances in the field of Linked (Open) Data (LOD), language data on the LOD cloud has grown in number, size, and variety. With an increased volume and variety of language data, optimizations of methods for distributing, storing, and querying these data become more central. To this end, this position paper investigates use cases at the intersection of LLOD and Big Data, existing approaches to utilizing Big Data techniques within the context of linked data, and discusses the challenges and benefits of this union.

Mots clés: Linguistic Linked Open Data (LLOD) Big Data Linguistic Data Science efficient processing; Linguistic Linked Open Data (LLOD); Big Data; Linguistic Data Science; efficient processing

Domaines: Informatique et langage [cs.CL]

Dimitar Trajanov, Elena-Simona Apostol, Radovan Garabík, Katerina Gkirtzou, Dagmar Gromann, et al.. From Linguistic Linked Data to Big Data. LREC-COLING 2024, ELDA, May 2024, Torino, Italy. ⟨hal-04541553⟩