# CatalogBank: A Structured and Interoperable Catalog Dataset with a Semi-Automatic Annotation Tool (DocumentLabeler) for Engineering System Design

arXiv: https://arxiv.org/abs/2408.08238 · HTML: https://mlsanity.com/paper/2408.08238/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems](https://mlsanity.com/paper/2602.16715/) (2026)
- [GENAI WORKBENCH: AI-Assisted Analysis and Synthesis of Engineering Systems from Multimodal Engineering Data](https://mlsanity.com/paper/2603.00251/) (2026)

## Abstract (arXiv)

In the realm of document engineering and Natural Language Processing (NLP), the integration of digitally born catalogs into product design processes presents a novel avenue for enhancing information extraction and interoperability. This paper introduces CatalogBank, a dataset developed to bridge the gap between textual descriptions and other data modalities related to engineering design catalogs. We utilized existing information extraction methodologies to extract product information from PDF-based catalogs to use in downstream tasks to generate a baseline metric. Our approach not only supports the potential automation of design workflows but also overcomes the limitations of manual data entry and non-standard metadata structures that have historically impeded the seamless integration of textual and other data modalities. Through the use of DocumentLabeler, an open-source annotation tool adapted for our dataset, we demonstrated the potential of CatalogBank in supporting diverse document-based tasks such as layout analysis and knowledge extraction. Our findings suggest that CatalogBank can contribute to document engineering and NLP by providing a robust dataset for training models capable of understanding and processing complex document formats with relatively less effort using the semi-automated annotation tool DocumentLabeler.