Natural language processing methods for knowledge management - Applying document clustering for fast search and grouping of engineering documents
Journal article, 2021

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.

engineering change request

document clustering

natural language processing

knowledge management

semantic data processing

Author

Ívar Örn Arnarsson

Chalmers, Industrial and Materials Science

Otto Frost

Fraunhofer-Chalmers Centre

Emil Gustavsson

Fraunhofer-Chalmers Centre

Mats Jirstrand

Fraunhofer-Chalmers Centre

Johan Malmqvist

Chalmers, Industrial and Materials Science, Product Development

Concurrent Engineering Research and Applications

1063-293X (ISSN)

Vol. In Press 1-11

Subject Categories

Mechanical Engineering

Areas of Advance

Production

DOI

10.1177/1063293X20982973

More information

Latest update

4/12/2021