Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics
This addresses a bottleneck in data-driven labour market analytics by providing a scalable alternative to hand-crafted ontologies, though it is incremental as it builds on existing language processing models.
The paper tackled the problem of reasoning across occupational data from different sources by developing an embedding-based alignment process that links free-form German job titles to established ontologies, achieving efficient approximate nearest-neighbour search for classification as a semantic search problem.
The limited ability to reason across occupational data from different sources is a long-standing bottleneck for data-driven labour market analytics. Previous research has relied on hand-crafted ontologies that allow such reasoning but are computationally expensive and require careful maintenance by human experts. The rise of language processing machine learning models offers a scalable alternative by learning shared semantic spaces that bridge diverse occupational vocabularies without extensive human curation. We present an embedding-based alignment process that links any free-form German job title to two established ontologies - the German Klassifikation der Berufe and the International Standard Classification of Education. Using publicly available data from the German Federal Employment Agency, we construct a dataset to fine-tune a Sentence-BERT model to learn the structure imposed by the ontologies. The enriched pairs (job title, embedding) define a similarity graph structure that we can use for efficient approximate nearest-neighbour search, allowing us to frame the classification process as a semantic search problem. This allows for greater flexibility, e.g., adding more classes. We discuss design decisions, open challenges, and outline ongoing work on extending the graph with other ontologies and multilingual titles.