Supporting Knowledge Re-Use with Effective Searches of Related Engineering Documents - A Comparison of Search Engine and Natural Language-Based Processing Algorithms
Paper in proceedings, 2019

Product development companies are collecting data in form of Engineering Change Requests for logged design issues and Design Guidelines to accumulate best practices. These documents are rich in unstructured data (e.g., free text) and previous research has pointed out that product developers find current it systems lacking capabilities to accurately retrieve relevant documents with unstructured data. In this research we compare the performance of Search Engine & Natural Language Processing algorithms in order to find fast related documents from two databases with Engineering Change Request and Design Guideline documents. The aim is to turn hours of manual documents searching into seconds by utilizing such algorithms to effectively search for related engineering documents and rank them in order of significance. Domain knowledge experts evaluated the results and it  shows that the models applied managed to find relevant documents with up to 90% accuracy of the cases tested. But accuracy varies based on selected algorithm and length of query.

Semantic data processing

Natural Language Processing

Machine learning

Knowledge management


Ívar Örn Arnarsson

Chalmers, Industrial and Materials Science

Otto Frost

Fraunhofer-Chalmers Centre

Emil Gustavsson

Fraunhofer-Chalmers Centre

Daniel Stenholm

Chalmers, Industrial and Materials Science, Product Development

Mats Jirstrand

Fraunhofer-Chalmers Centre

Johan Malmqvist

Chalmers, Industrial and Materials Science, Product Development

Proceedings of the International Conference on Engineering Design, ICED

22204334 (ISSN) 22204342 (eISSN)


22nd International Conference on Engineering Design, ICED 2019
Delft, Netherlands,

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Areas of Advance

Information and Communication Technology




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