# Semi-Supervised Taxonomy Expansion and Completion in Dynamic Taxonomies
## 1 Introduction
For instance, adding specific hyponyms like 'Python' or 'Rust' under 'Programming Languages' expands the breadth of the category while ensuring consistency and logical alignment. Maintaining logical consistency in both vertical and horizontal expansions while avoiding redundancy is another significant challenge. For example, placing 'Machine Learning' under both 'Artificial Intelligence' and 'Data Science' might create overlaps, leading to confusion. Similarly, deciding whether 'Deep Learning' is a subcategory of 'Machine Learning' or a separate domain altogether requires careful consideration to ensure the taxonomy remains coherent and non-redundant. To enhance the credibility and effectiveness of taxonomy expansion and completion, we propose a novel methodology leveraging a Retrieval-Augmented Generation (RAG) framework with a human-in-the-loop. RAG combines the precision of retrieval-based methods, which are adept at accessing structured knowledge, with the generative power of large language models, enabling context-aware and dynamic taxonomy updates. Our approach facilitates the seamless integration of new concepts into existing taxonomies while accurately matching them to related concepts already present, thereby avoiding redundancy and preserving structural consistency. By harnessing contextual embeddings and incorporating domain-specific knowledge, the methodology supports both vertical and horizontal expansion. Additionally, it addresses taxonomy completion by identifying and filling missing hierarchical relationships. A key distinguishing feature of our work is the design of RAG-based baselines for taxonomy management. Unlike traditional supervised approaches, our baselines are designed to handle semi-supervised tasks, leveraging retrieval mechanisms to augment generative models with relevant knowledge and then relying on a human-in-the-loop to reassure taxonomy credibility with a significant reduction in workload. Additionally, we introduce a novel dataset derived from job postings in the automotive sector, tailored to evaluate taxonomy expansion and completion tasks in a real-world domain. This dataset reflects the complexities of dynamic skill landscapes, including ambiguities, sparse data, and domain-specific variations, making it a valuable resource for benchmarking future methods. * We introduce a comprehensive framework for taxonomy expansion and completion, integrating retrieval and generation in a unified approach tailored to dynamic and large-scale taxonomies. * Our work establishes baseline models that demonstrate the effectiveness of retrieval-augmented generation for handling real-world taxonomy management tasks. * We present a dataset derived from job postings in the automotive sector, reflecting real-world complexities and serving as a benchmark for evaluating taxonomy expansion and completion methodologies. * The proposed framework supports both vertical (hypernym addition) and horizontal (hyponym expansion) updates while addressing missing relationships to ensure logical consistency. The remainder of this paper is organized as follows: Section 2 formally defines the taxonomy expansion and completion problem, detailing the objectives and expected outputs. Section 3 reviews related work and highlights current limitations in the literature. Section 4 outlines our methodology, including dataset construction, system design, evaluation protocols, and the experimental results, while Section 5 provides an in-depth discussion and interpretation of the findings. Finally, Section 6 concludes the study and outlines directions for future work.