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    <abstract>Relation extraction has the potential for large-scale knowledge graph construction, but current methods do not consider the qualifier attributes for each relation triplet, such as time, quantity or location. The qualifiers form hyper-relational facts which better capture the rich and complex knowledge graph structure. For example, the relation triplet (Leonard Parker, Educated At, Harvard University) can be factually enriched by including the qualifier (End Time, 1967). Hence, we propose the task of hyper-relational extraction to extract more specific and complete facts from text. To support the task, we construct HyperRED, a large-scale and general-purpose dataset. Existing models cannot perform hyper-relational extraction as it requires a model to consider the interaction between three entities. Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers. To improve model scalability and reduce negative class imbalance, we further propose a cube-pruning method. Our experiments show that CubeRE outperforms strong baselines and reveal possible directions for future research. Our code and data are available at github.com/declare-lab/HyperRED.</abstract>
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%0 Conference Proceedings
%T A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach
%A Chia, Yew Ken
%A Bing, Lidong
%A Aljunied, Sharifah Mahani
%A Si, Luo
%A Poria, Soujanya
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chia-etal-2022-dataset
%X Relation extraction has the potential for large-scale knowledge graph construction, but current methods do not consider the qualifier attributes for each relation triplet, such as time, quantity or location. The qualifiers form hyper-relational facts which better capture the rich and complex knowledge graph structure. For example, the relation triplet (Leonard Parker, Educated At, Harvard University) can be factually enriched by including the qualifier (End Time, 1967). Hence, we propose the task of hyper-relational extraction to extract more specific and complete facts from text. To support the task, we construct HyperRED, a large-scale and general-purpose dataset. Existing models cannot perform hyper-relational extraction as it requires a model to consider the interaction between three entities. Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers. To improve model scalability and reduce negative class imbalance, we further propose a cube-pruning method. Our experiments show that CubeRE outperforms strong baselines and reveal possible directions for future research. Our code and data are available at github.com/declare-lab/HyperRED.
%R 10.18653/v1/2022.emnlp-main.688
%U https://aclanthology.org/2022.emnlp-main.688
%U https://doi.org/10.18653/v1/2022.emnlp-main.688
%P 10114-10133

Markdown (Informal)

[A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach](https://aclanthology.org/2022.emnlp-main.688) (Chia et al., EMNLP 2022)

• A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach (Chia et al., EMNLP 2022)

ACL

• Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied, Luo Si, and Soujanya Poria. 2022. A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10114–10133, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.