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

Machine learning

Natural Language Processing

Semantic data processing

Knowledge management


Ívar Örn Arnarsson

Chalmers, Industri- och materialvetenskap

Otto Frost

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Emil Gustavsson

Chalmers, Matematiska vetenskaper

Daniel Stenholm

Chalmers, Industri- och materialvetenskap, Produktutveckling

Mats Jirstrand

Chalmers, Elektroteknik, System- och reglerteknik

Johan Malmqvist

Chalmers, Industri- och materialvetenskap, Produktutveckling

Proceedings of the International Conference on Engineering Design, ICED

22204334 (ISSN) 22204342 (eISSN)

Vol. 1 1 2597-2606

The 22nd International Conference on Engineering Design
Delft, Netherlands,


Produktionsteknik, arbetsvetenskap och ergonomi


Informations- och kommunikationsteknik




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