@inproceedings{trajanov-etal-2024-linguistic,
title = "From Linguistic Linked Data to Big Data",
author = "Trajanov, Dimitar and
Apostol, Elena and
Garabik, Radovan and
Gkirtzou, Katerina and
Gromann, Dagmar and
Liebeskind, Chaya and
Palma, Cosimo and
Rosner, Michael and
Sampri, Alexia and
S{\'e}rasset, Gilles and
Spahiu, Blerina and
Truic{\u{a}}, Ciprian-Octavian and
Valunaite Oleskeviciene, Giedre",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.661/",
pages = "7489--7502",
abstract = "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."
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%0 Conference Proceedings
%T From Linguistic Linked Data to Big Data
%A Trajanov, Dimitar
%A Apostol, Elena
%A Garabik, Radovan
%A Gkirtzou, Katerina
%A Gromann, Dagmar
%A Liebeskind, Chaya
%A Palma, Cosimo
%A Rosner, Michael
%A Sampri, Alexia
%A Sérasset, Gilles
%A Spahiu, Blerina
%A Truică, Ciprian-Octavian
%A Valunaite Oleskeviciene, Giedre
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F trajanov-etal-2024-linguistic
%X 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.
%U https://aclanthology.org/2024.lrec-main.661/
%P 7489-7502
Markdown (Informal)
[From Linguistic Linked Data to Big Data](https://aclanthology.org/2024.lrec-main.661/) (Trajanov et al., LREC-COLING 2024)
ACL
- Dimitar Trajanov, Elena Apostol, Radovan Garabik, Katerina Gkirtzou, Dagmar Gromann, Chaya Liebeskind, Cosimo Palma, Michael Rosner, Alexia Sampri, Gilles Sérasset, Blerina Spahiu, Ciprian-Octavian Truică, and Giedre Valunaite Oleskeviciene. 2024. From Linguistic Linked Data to Big Data. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7489–7502, Torino, Italia. ELRA and ICCL.