T axo P ro: A Plug-In L o RA -based Cross-Domain Method for Low-Resource Taxonomy Completion (2025)

Abstract: Low-resource taxonomy completion aims to automatically insert new concepts into the existing taxonomy, in which only a few in-domain training samples are available. Recent studies have achieved considerable progress by incorporating prior knowledge from pre-trained language models (PLMs). However, these studies tend to overly rely on such knowledge and neglect the shareable knowledge across different taxonomies. In this paper, we propose TaxoPro, a plug-in LoRA-based cross-domain method, that captures shareable knowledge from the high- resource taxonomy to improve PLM-based low-resource taxonomy completion techniques. To prevent negative interference between domain-specific and domain-shared knowledge, TaxoPro decomposes cross- domain knowledge into domain-shared and domain-specific components, storing them using low-rank matrices (LoRA). Additionally, TaxoPro employs two auxiliary losses to regulate the flow of shareable knowledge. Experimental results demonstrate that TaxoPro improves PLM-based techniques, achieving state-of-the-art performance in completing low-resource taxonomies. Code is available at https://github.com/cyclexu/TaxoPro.

## introduction taxonomies are knowledge structures that hierarchically organize concepts through hypernymhyponym (''is-a'') relations. they find extensive applications in fields such as natural language processing @xcite , recommendation systems @xcite , and information retrieval @xcite . most current taxonomies are manually curated by domain experts, which is both time-consuming and labour-intensive. with the constant emergence of new concepts, keeping taxonomies up-to-date for downstream applications has become a critical challenge @xcite . to solve this problem, significant effort has been dedicated to the taxonomy expansion task @xcite @xcite . in this task, the existing taxonomy is expanded by inserting the new concept (query) to the most appropriate hypernym (parent) within the existing taxonomy as a leaf node. however, recent researchers contend that the ''leaf-only'' assumption is unsuitable @xcite , leading to significant limitations in real-world scenarios @xcite . thus, they turn to the taxonomy completion task @xcite @xcite , where the query is inserted between a pair of hypernym and hyponym (child). for example, the query ''wearable device'' is inserted between the parent ''electronic equipment'' and the child ''ar glass'' as shown in figure 1 . in practical scenarios, the low-resource setting, where only a limited number of concepts exist in the existing taxonomy, is prevalent as most taxonomies typically comprise around a thousand concepts @xcite . under such a setting, the early taxonomy expansion and completion methods @xcite @xcite suffer from performance degradation due to insufficient training samples @xcite . several studies @xcite @xcite have shown that this can be mitigated by incorporating prior knowledge from the pre-trained language model (plm). however, such knowledge could be generic and irrelevant to the taxonomy tasks @xcite , thus limiting the performance of these plm-based techniques. meanwhile, taxonomies across different domains store the same type of knowledge, i.e., hierarchical relations between concepts. consequently, the high-resource taxonomy can serve as an extra knowledge base for completing the low-resource taxonomy. in this paper, we explore the research question of how to capture knowledge from the high-resource taxonomy for the plm-based low-resource taxonomy completion enhancement? inspired by the recent studies @xcite @xcite ) that utilize the parameter-efficient fine-tuning (peft) techniques @xcite for knowledge storage and transfer, we utilize lora @xcite , a widely used peft technique that leverages low-rank matrices as the knowledge update, to capture domain-shared knowledge from a high-resource source taxonomy and apply it to complete a low-resource target taxonomy. specifically, we propose a lora-based cross-domain method that can be plugged into the plm-based taxonomy completion techniques. our method has two main modules: (i) knowledge decomposition and (ii) shareable knowledge flow control. in the first module, we decompose the knowledge of each taxonomy into domain-shared and domain-specific components to prevent negative interference between these two types of knowledge @xcite . we store these components using separate low-rank matrices, which are updated through task-specific losses across various domains. in the second module, we employ two auxiliary losses to guide the flow of shareable knowledge. specifically, we pull shareable knowledge into the domain-shared matrices and push it out from the domain-specific matrices. our objective is to maximize the extraction of shareable knowledge from the high-resource taxonomy, thereby improving the completion of the low-resource taxonomy. we plug the proposed method into two representative plm-based taxonomy completion techniques and conduct extensive experiments on three low-resource taxonomy datasets. experimental results show that our method improves the plm-based techniques and achieves state-of-the-art performance in low-resource taxonomy completion. in summary, our contributions include: ## methodology in this section, we propose a cross-domain method that can be plugged into the plm-based taxonomy completion techniques ( §3.2). the method comprises a knowledge decomposition module ( §4.1) and a shareable knowledge flow control module ( §4.2), as illustrated in figure 2 . ## experimental settings in this section, we detail our experimental settings. implementation details are in appendix a.3. ## conclusion in this paper, we propose taxopro, a lora-based plug-in cross-domain method. it leverages shareable knowledge from the high-resource taxonomy to enhance plm-based techniques in low-resource taxonomy completion. we decompose cross-domain knowledge into domain-shared and domain-specific parts, storing them with the low-rank matrices to avoid negative interference. two auxiliary losses direct the flow of shareable knowledge. experiments prove taxopro's effectiveness. we believe our initial exploration of cross-domain taxonomy completion presents an interesting direction for the community.