@inproceedings{di-buono-etal-2020-terme,
title = "Terme-{\`a}-{LLOD}: Simplifying the Conversion and Hosting of Terminological Resources as Linked Data",
author = "di Buono, Maria Pia and
Cimiano, Philipp and
Elahi, Mohammad Fazleh and
Grimm, Frank",
editor = "Ionov, Maxim and
McCrae, John P. and
Chiarcos, Christian and
Declerck, Thierry and
Bosque-Gil, Julia and
Gracia, Jorge",
booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.ldl-1.5",
pages = "28--35",
abstract = "In recent years, there has been increasing interest in publishing lexicographic and terminological resources as linked data. The benefit of using linked data technologies to publish terminologies is that terminologies can be linked to each other, thus creating a cloud of linked terminologies that cross domains, languages and that support advanced applications that do not work with single terminologies but can exploit multiple terminologies seamlessly. We present Terme-{`}a-LLOD (TAL), a new paradigm for transforming and publishing terminologies as linked data which relies on a virtualization approach. The approach rests on a preconfigured virtual image of a server that can be downloaded and installed. We describe our approach to simplifying the transformation and hosting of terminological resources in the remainder of this paper. We provide a proof-of-concept for this paradigm showing how to apply it to the conversion of the well-known IATE terminology as well as to various smaller terminologies. Further, we discuss how the implementation of our paradigm can be integrated into existing NLP service infrastructures that rely on virtualization technology. While we apply this paradigm to the transformation and hosting of terminologies as linked data, the paradigm can be applied to any other resource format as well.",
language = "English",
ISBN = "979-10-95546-36-8",
}
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%0 Conference Proceedings
%T Terme-à-LLOD: Simplifying the Conversion and Hosting of Terminological Resources as Linked Data
%A di Buono, Maria Pia
%A Cimiano, Philipp
%A Elahi, Mohammad Fazleh
%A Grimm, Frank
%Y Ionov, Maxim
%Y McCrae, John P.
%Y Chiarcos, Christian
%Y Declerck, Thierry
%Y Bosque-Gil, Julia
%Y Gracia, Jorge
%S Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-36-8
%G English
%F di-buono-etal-2020-terme
%X In recent years, there has been increasing interest in publishing lexicographic and terminological resources as linked data. The benefit of using linked data technologies to publish terminologies is that terminologies can be linked to each other, thus creating a cloud of linked terminologies that cross domains, languages and that support advanced applications that do not work with single terminologies but can exploit multiple terminologies seamlessly. We present Terme-‘a-LLOD (TAL), a new paradigm for transforming and publishing terminologies as linked data which relies on a virtualization approach. The approach rests on a preconfigured virtual image of a server that can be downloaded and installed. We describe our approach to simplifying the transformation and hosting of terminological resources in the remainder of this paper. We provide a proof-of-concept for this paradigm showing how to apply it to the conversion of the well-known IATE terminology as well as to various smaller terminologies. Further, we discuss how the implementation of our paradigm can be integrated into existing NLP service infrastructures that rely on virtualization technology. While we apply this paradigm to the transformation and hosting of terminologies as linked data, the paradigm can be applied to any other resource format as well.
%U https://aclanthology.org/2020.ldl-1.5
%P 28-35
Markdown (Informal)
[Terme-à-LLOD: Simplifying the Conversion and Hosting of Terminological Resources as Linked Data](https://aclanthology.org/2020.ldl-1.5) (di Buono et al., LDL 2020)
ACL