Supporting knowledge re-use with effective searches of related engineering documents - a comparison of Search Engine and Natural Language Processing-based algorithms

DS 94: Proceedings of the Design Society: 22nd International Conference on Engineering Design (ICED19)

Year: 2019
Editor: Wartzack, Sandro; Schleich, Benjamin; Gon
Author: Arnarsson, Ivar
Series: ICED
Institution: Volvo Trucks / Chalmers
Section: Knowledge-based engineering
DOI number:
ISSN: 2220-4342


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.

Keywords: Natural Language Processing, Semantic data processing, Machine learning, Knowledge management


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