A Single Vector Is Not Enough: Taxonomy Expansion via Box Embeddings

Jiang, Song; Yao, Qiyue; Wang, Qifan; Sun, Yizhou

doi:10.1145/3543507.3583310

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Based on the assumption of the problem, the studies can be divided into two main categories: taxonomy expansion and completion. In taxonomy expansion, researchers Yu et al, 2020;Ma et al, 2021;Wang et al, 2021a;Liu et al, 2021;Takeoka et al, 2021;Zhai et al, 2023;Jiang et al, 2023;Cheng et al, 2022;Phukon et al, 2022) suggest that new concepts can only be added as leaf nodes. However, this assumption is inappropriate for real-world applications: the query nodes can also be added as non-leaf nodes to the taxonomy (Zhang et al, 2021).…”

Section: Taxonomy Expansion and Completionmentioning

confidence: 99%

TacoPrompt: A Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion

Xu,

Liu,

Niu

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Automatic taxonomy completion aims to attach the emerging concept to an appropriate pair of hypernym and hyponym in the existing taxonomy. Existing methods suffer from the overfitting to leaf-only problem caused by imbalanced leaf and non-leaf samples when training the newly initialized classification head. Besides, they only leverage subtasks, namely attaching the concept to its hypernym or hyponym, as auxiliary supervision for representation learning yet neglect the effects of subtask results on the final prediction. To address the aforementioned limitations, we propose TacoPrompt, a Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion. First, we perform triplet semantic matching using the prompt learning paradigm to effectively learn non-leaf attachment ability from imbalanced training samples. Second, we design the result context to relate the final prediction to the subtask results by a contextual approach, enhancing prompt-based multi-task learning. Third, we leverage a two-stage retrieval and re-ranking approach to improve the inference efficiency. Experimental results on three datasets show that TacoPrompt achieves state-of-the-art taxonomy completion performance. Codes are available at https://github.com/cyclexu/TacoPrompt.

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Section: Taxonomy Expansion and Completionmentioning

confidence: 99%

TacoPrompt: A Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion

Xu,

Liu,

Niu

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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PEB-TAXO: Projecting Entities as Boxes for Taxonomy Expansion

Zhang,

Qin,

Feng

2024

Neural Process Lett

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As domain knowledge evolves, new concepts (entities) continuously emerge, leading to a decrease in the coverage of existing taxonomies with hierarchical structures, thus necessitating the continual expansion of these taxonomies to include new concepts. Due to the relationships (“contain”, “disjoint”, and “intersect”) between the boxes, which can effectively represent asymmetric hierarchies, box embeddings have been successfully applied in taxonomy expansion. However, existing models that use box embeddings for taxonomy expansion have the following shortcomings: (1) the size of the boxes is not restricted, and the model produces meaningless boxes; (2) the model does not fully utilize the geometric information of the boxes. To address the above shortcomings, this paper proposes a taxonomy expansion model based on projecting entities as boxes: PEB-TAXO. Firstly, PEB-TAXO employs modified L1 regularization to constrain the box sizes in all dimensions, pushing the box sizes towards the preset minimum, thereby avoiding the generation of meaningless boxes by the model. Secondly, the model utilizes a box inclusion inference method: it infers the relationship between two entities through the relationship between two boxes in geometric space, thus fully exploiting the geometric information of the boxes for more accurate inferences. Finally, we conducted extensive experiments on two public datasets and verified that PEB-TAXO greatly improves performance over mainstream taxonomy expansion methods.

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