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From linguistic linked data to big data

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Bibliographic Details
Title: From linguistic linked data to big data
Authors: Trajanov, Dimitar, Apostol, Elena-Simona, Garabík, Radovan, Gkirtzou, Katerina, Gromann, Dagmar, Liebeskind, Chaya, Palma, Cosimo, Rosner, Michael, Sampri, Alexia, Sérasset, Gilles, Spahiu, Blerina, Truică, Ciprian-Octavian, Oleskeviciene, Giedre Valunaite, 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Publication Year: 2024
Collection: University of Malta: OAR@UM / L-Università ta' Malta
Subject Terms: Linked data, Big data, Computer-aided engineering, Semantic Web, Data structures (Computer science)
Description: 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. ; peer-reviewed
Document Type: conference object
Language: English
Relation: https://www.um.edu.mt/library/oar/handle/123456789/130471
Availability: https://www.um.edu.mt/library/oar/handle/123456789/130471
Rights: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
Accession Number: edsbas.414C3E0D
Database: BASE
Description
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. ; peer-reviewed