ECLASS-Augmented Semantic Product Search for Electronic Components
Nico Baumgart, Markus Lange-Hegermann, Jan Henze

TL;DR
This paper evaluates LLM-assisted dense retrieval for industrial electronic component search, showing that integrating ECLASS hierarchical semantics significantly improves retrieval effectiveness over traditional methods.
Contribution
It systematically assesses the combination of dense retrieval and ECLASS semantic augmentation, demonstrating substantial performance improvements in industrial product search.
Findings
Dense retrieval with re-ranking outperforms lexical methods and web-search baselines.
Augmenting representations with ECLASS semantics improves retrieval accuracy.
Achieves Hit_Rate@5 of 94.3%, surpassing BM25 and foundation models.
Abstract
Efficient semantic access to industrial product data is a key enabler for factory automation and emerging LLM-based agent workflows, where both human engineers and autonomous agents must identify suitable components from highly structured catalogs. However, the vocabulary mismatch between natural-language queries and attribute-centric product descriptions limits the effectiveness of traditional retrieval approaches, e.g., BM25. In this work, we present a systematic evaluation of LLM-assisted dense retrieval for semantic product search on industrial electronic components, and investigate the integration of hierarchical semantics from the ECLASS standard into embedding-based retrieval. Our results show that dense retrieval combined with re-ranking substantially outperforms classical lexical methods and foundation model web-search baselines. In particular, the proposed approach achieves a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
