# Semi-Supervised Taxonomy Expansion and Completion in Dynamic Taxonomies
## 1 Introduction  
A taxonomy is a structured classification system that organizes and represents concepts, entities, or knowledge hierarchically [1, 2]. Each element in a taxonomy is defined by its relationships to other elements, commonly through \"is-a\" or \"part-of\" relationships. For instance, in skill-based taxonomies, \"Programming Languages\" is a category that includes specific programming skills like Python, JavaScript, and C++. Similarly, \"Machine Learning Techniques\" can encompass \"Supervised Learning\", \"Unsupervised Learning\", and \"Reinforcement Learning\". Taxonomies are inherently dynamic, requiring continuous updates to accommodate the evolution of knowledge and the emergence of new technologies. Our work specifically focuses on highly volatile and dynamic skill-based taxonomies. Skill-based taxonomies are particularly volatile due to the rapidly shifting job market and technological advancements. For instance, emerging skills such as 'Quantum Computing' or 'Prompt Engineering' must be incorporated promptly to ensure relevance and usability. This rapid evolution underscores the difficulty of manual taxonomy curation, a process that is labor-intensive, time-consuming, and often incomplete. Skill-based taxonomies are pivotal in various domains, providing clarity and structure for applications such as curriculum design, skills assessments, and job matching in the professional sector. Due to this, automated taxonomy management has received substantial attention, particularly for tasks such as taxonomy expansion [3] and completion [4, 5]. Taxonomyexpansion focuses on extracting and modeling hierarchical information from existing taxonomies, employing techniques such as local egonets [3], parent-query-child triplets [5], and mini-path encodings [6] to capture structural relationships. Taxonomy completion techniques, on the other hand, leverage external corpora to generate embeddings for new concepts, relying either on implicit relational semantics or seed-guided methods to construct limited taxonomies. Earlier approaches have explored combining semantic sentences and subgraph representations, often using methods like lightweight multi-layer perceptrons (MLPs) [7]. Additionally, frameworks such as TaxoExpan [3] and TaxoEnrich [4] have utilized structural and semantic features, incorporating position-aware encoders and pretrained language models to tackle taxonomy completion and expansion tasks. Despite these advancements, applying automated methods to dynamic and rapidly evolving domains like professional skills introduces unique challenges. Skills are inherently ambiguous, context-dependent, and often represented by sparse data, making it difficult to determine their placement within a taxonomy. One significant challenge in managing skill-based taxonomies is the variability in how people express the same skills or knowledge. The same skill can be written in multiple ways--such as 'Data Analysis,' 'Analyzing Data,' or 'Data Analytics'--depending on individual or organizational preferences. This variability becomes even more pronounced when skills are extracted by generative large language models, which can produce diverse representations of the same concept based on context or prompts. These methods rely heavily on exact matches, predefined rules, or limited synonym lists, which are insufficient to capture the breadth and nuance of such variations. Furthermore, skill taxonomies must handle both vertical expansion (introducing broader categories/hypernyms) and horizontal expansion (adding specific variations/hyponyms) within the categories while maintaining logical consistency and avoiding redundancy. Vertical expansion involves introducing broader categories to accommodate higher-level relationships, such as adding 'Technology Skills' as a hypernym for categories like 'Programming' or 'Data Analysis.' Horizontal expansion, on the other hand, requires adding more specific terms grouped within existing categories.