@inproceedings{saini-etal-2025-relational,
title = "Are Relational Triple Extraction Frameworks Sufficient for Hyper-relational Facts ?",
author = "Saini, Pratik and
Sarkar, Chayan and
Nayak, Tapas",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-short.10/",
pages = "112--119",
ISBN = "979-8-89176-299-2",
abstract = "Hyper-relational fact extraction involves identifying relational triples along with additional contextual information{---}known as qualifiers{---}such as time, location, or quantity. These qualifiers enable models to represent complex real-world knowledge more accurately. While numerous end-to-end models have been developed for extracting relational triples, they are not designed to handle qualifiers directly. In this work, we propose a straightforward and effective approach to extend existing end-to-end triple extraction models to also capture qualifiers. Our method reformulates qualifiers as new relations by computing the Cartesian product between qualifiers and their associated relations. This transformation allows the model to extract qualifier information as additional triples, which can later be merged to form complete hyper-relational facts. We evaluate our approach using multiple end-to-end triple extraction models on the HyperRED dataset and demonstrate its effectiveness in extracting hyper-relational facts."
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<abstract>Hyper-relational fact extraction involves identifying relational triples along with additional contextual information—known as qualifiers—such as time, location, or quantity. These qualifiers enable models to represent complex real-world knowledge more accurately. While numerous end-to-end models have been developed for extracting relational triples, they are not designed to handle qualifiers directly. In this work, we propose a straightforward and effective approach to extend existing end-to-end triple extraction models to also capture qualifiers. Our method reformulates qualifiers as new relations by computing the Cartesian product between qualifiers and their associated relations. This transformation allows the model to extract qualifier information as additional triples, which can later be merged to form complete hyper-relational facts. We evaluate our approach using multiple end-to-end triple extraction models on the HyperRED dataset and demonstrate its effectiveness in extracting hyper-relational facts.</abstract>
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%0 Conference Proceedings
%T Are Relational Triple Extraction Frameworks Sufficient for Hyper-relational Facts ?
%A Saini, Pratik
%A Sarkar, Chayan
%A Nayak, Tapas
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-299-2
%F saini-etal-2025-relational
%X Hyper-relational fact extraction involves identifying relational triples along with additional contextual information—known as qualifiers—such as time, location, or quantity. These qualifiers enable models to represent complex real-world knowledge more accurately. While numerous end-to-end models have been developed for extracting relational triples, they are not designed to handle qualifiers directly. In this work, we propose a straightforward and effective approach to extend existing end-to-end triple extraction models to also capture qualifiers. Our method reformulates qualifiers as new relations by computing the Cartesian product between qualifiers and their associated relations. This transformation allows the model to extract qualifier information as additional triples, which can later be merged to form complete hyper-relational facts. We evaluate our approach using multiple end-to-end triple extraction models on the HyperRED dataset and demonstrate its effectiveness in extracting hyper-relational facts.
%U https://aclanthology.org/2025.ijcnlp-short.10/
%P 112-119
Markdown (Informal)
[Are Relational Triple Extraction Frameworks Sufficient for Hyper-relational Facts ?](https://aclanthology.org/2025.ijcnlp-short.10/) (Saini et al., IJCNLP-AACL 2025)
ACL
- Pratik Saini, Chayan Sarkar, and Tapas Nayak. 2025. Are Relational Triple Extraction Frameworks Sufficient for Hyper-relational Facts ?. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 112–119, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.