# Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics

arXiv: https://arxiv.org/abs/2509.04942 · HTML: https://mlsanity.com/paper/2509.04942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Matryoshka Representation Learning](https://mlsanity.com/paper/2205.13147/) (2022)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [JobBERT: Understanding Job Titles through Skills](https://mlsanity.com/paper/2109.09605/) (2021)
- [A Comparison of Optimization Algorithms for Deep Learning](https://mlsanity.com/paper/2007.14166/) (2020)
- [Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning](https://mlsanity.com/paper/2009.07429/) (2020)
- [Learning Job Titles Similarity from Noisy Skill Labels](https://mlsanity.com/paper/2207.00494/) (2022)
- [A Study of Checkpointing in Large Scale Training of Deep Neural Networks](https://mlsanity.com/paper/2012.00825/) (2020)
- [Job Posting-Enriched Knowledge Graph for Skills-based Matching](https://mlsanity.com/paper/2109.02554/) (2021)
- [Skill-driven Recommendations for Job Transition Pathways](https://mlsanity.com/paper/2011.11801/) (2020)
- [LLM+KG@VLDB'24 Workshop Summary](https://mlsanity.com/paper/2410.01978/) (2024)

## Abstract (arXiv)

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.