Inferring Semantic Relations Between Terms with Large Language Models

Giulia Speranza

Inferring Semantic Relations Between Terms with Large Language Models

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Abstract

The purpose of this paper is to investigate the ability of Large Language Models (LLMs) to identify relations among terms, with the goal of facilitating and accelerating the construction of thesauri and terminological resources. We investigate whether the use of LLMs in this context can provide a valuable initial set of relations, serving as a basis upon which professional terminologists can build, validate, and enrich domain-specific knowledge representations.

Anthology ID:: 2025.termtrends-1.3
Volume:: Proceedings of the 5th Conference on Language, Data and Knowledge: TermTrends 2025
Month:: September
Year:: 2025
Address:: Naples, Italy
Editors:: Katerina Gkirtzou, Slavko Žitnik, Jorge Gracia, Dagmar Gromann, Maria Pia di Buono, Johanna Monti, Maxim Ionov
Venues:: termtrends | WS
SIG:
Publisher:: Unior Press
Note:
Pages:: 25–30
Language:
URL:: https://aclanthology.org/2025.termtrends-1.3/
DOI:
Bibkey:
Cite (ACL):: Giulia Speranza. 2025. Inferring Semantic Relations Between Terms with Large Language Models. In Proceedings of the 5th Conference on Language, Data and Knowledge: TermTrends 2025, pages 25–30, Naples, Italy. Unior Press.
Cite (Informal):: Inferring Semantic Relations Between Terms with Large Language Models (Speranza, termtrends 2025)
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PDF:: https://aclanthology.org/2025.termtrends-1.3.pdf

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@inproceedings{speranza-2025-inferring,
    title = "Inferring Semantic Relations Between Terms with Large Language Models",
    author = "Speranza, Giulia",
    editor = "Gkirtzou, Katerina  and
      {\v{Z}}itnik, Slavko  and
      Gracia, Jorge  and
      Gromann, Dagmar  and
      di Buono, Maria Pia  and
      Monti, Johanna  and
      Ionov, Maxim",
    booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge: TermTrends 2025",
    month = sep,
    year = "2025",
    address = "Naples, Italy",
    publisher = "Unior Press",
    url = "https://aclanthology.org/2025.termtrends-1.3/",
    pages = "25--30",
    ISBN = "978-88-6719-334-9",
    abstract = "The purpose of this paper is to investigate the ability of Large Language Models (LLMs) to identify relations among terms, with the goal of facilitating and accelerating the construction of thesauri and terminological resources. We investigate whether the use of LLMs in this context can provide a valuable initial set of relations, serving as a basis upon which professional terminologists can build, validate, and enrich domain-specific knowledge representations."
}

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%0 Conference Proceedings
%T Inferring Semantic Relations Between Terms with Large Language Models
%A Speranza, Giulia
%Y Gkirtzou, Katerina
%Y Žitnik, Slavko
%Y Gracia, Jorge
%Y Gromann, Dagmar
%Y di Buono, Maria Pia
%Y Monti, Johanna
%Y Ionov, Maxim
%S Proceedings of the 5th Conference on Language, Data and Knowledge: TermTrends 2025
%D 2025
%8 September
%I Unior Press
%C Naples, Italy
%@ 978-88-6719-334-9
%F speranza-2025-inferring
%X The purpose of this paper is to investigate the ability of Large Language Models (LLMs) to identify relations among terms, with the goal of facilitating and accelerating the construction of thesauri and terminological resources. We investigate whether the use of LLMs in this context can provide a valuable initial set of relations, serving as a basis upon which professional terminologists can build, validate, and enrich domain-specific knowledge representations.
%U https://aclanthology.org/2025.termtrends-1.3/
%P 25-30

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Markdown (Informal)

[Inferring Semantic Relations Between Terms with Large Language Models](https://aclanthology.org/2025.termtrends-1.3/) (Speranza, termtrends 2025)

Inferring Semantic Relations Between Terms with Large Language Models (Speranza, termtrends 2025)

ACL

Giulia Speranza. 2025. Inferring Semantic Relations Between Terms with Large Language Models. In Proceedings of the 5th Conference on Language, Data and Knowledge: TermTrends 2025, pages 25–30, Naples, Italy. Unior Press.