# Semi-Supervised Taxonomy Expansion and Completion in Dynamic Taxonomies
## 3 Related Work  
In many real-world applications, taxonomies are meticulously created by experts or through crowdsourcing efforts and are often integrated into online systems [8; 9]. Rather than constructing taxonomies from scratch, these systems require dynamic methods to expand existing taxonomies and ensure they remain relevant. Previous research has tackled this by incorporating new concepts into resources like WordNet, using named entities from Wikipedia [10] or domain-specific concepts derived from various fields [11; 12]. For example, Task 14 of the SemEval 2016 challenge [13] aimed to enrich WordNet with concepts from domains such as health, sports, and finance. However, these methods heavily rely on WordNet's unique structure, making it difficult to generalize them to other taxonomies. To address this limitation, recent studies have proposed methods for expanding generic taxonomies. [PERSON] et al. [14] introduced a model that uses search engine query logs to grow taxonomies, while [PERSON] et al. [15] developed models that identify concept variations using external datasets. [PERSON] et al. [16] created a ranking model combining features, including data retrieved from external APIs, to identify the best positions for new concepts. Similarly, [PERSON] et al. [17] employed hyperbolic embeddings to link new concepts to the most related nodes in a taxonomy. Recent taxonomy expansion methods typically focus on constructing an entire taxonomy through two primary steps. The first one involves identifying hypernym/hyponym pairs using either pattern-based methods [18; 19] with predefined patterns or distributional  
Figure 1: The Figure gives a visual illustration of both taxonomy expansion and the completion process. methods [20; 21] that calculate term similarities based on embeddings. The second step organizes these relations into a hierarchical structure, such as a tree or Directed Acyclic Graph (DAG), using optimization techniques like maximum spanning trees [22; 23], optimal branching [24], or minimum-cost flow [25]. Additionally, some methods utilize entity set expansion to incrementally grow taxonomies from a small seed [26]. [PERSON] et al. [3] in their approach eliminated the need for external data by leveraging the existing taxonomy for self-supervision. This approach enabled its application across diverse domains and incorporated the local structural context of each candidate position. However, for our application of skills, this approach is not reliable as expert supervision is still required in the process. In contrast, our approach simplifies this process by constructing a single-level tree as a knowledge base. For each query representing a skill, the system retrieves the most relevant branch based on the query's hypernym, definition and related branch hyponyms. This streamlined methodology avoids building complex hierarchical structures, focusing instead on direct retrieval and representation for specific skills. This approach is particularly suited to dynamic and domain-specific requirements. This distinction is critical because, in dynamic and volatile taxonomies, entirely new categories frequently emerge. For instance, consider concepts like \"quantum computing\" or \"carbon-neutral technology,\" which represent terms previously absent from existing taxonomies but that should now be incorporated as new hypernyms. Without a systematic approach to identify and classify such emergent categories as new hypernyms, taxonomies risk becoming outdated and irrelevant. The ability to differentiate between new hypernyms, new hyponyms, and existing hyponyms is essential to maintain the relevance and accuracy of taxonomies. Current methods often fail in this regard by assuming all new concepts are simply hyponyms of existing categories, ignoring the possibility of entirely new parent categories. To address this gap, we reformulated the task into a classification problem with three labels: (1) new hypernym, (2) new hyponym, and (3) existing hyponym, as detailed in Section 4.