Natural language processing methods for knowledge management - Applying document clustering for fast search and grouping of engineering documents
Artikel i vetenskaplig tidskrift, 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

semantic data processing

natural language processing

knowledge management


Ívar Örn Arnarsson

Chalmers, Industri- och materialvetenskap

Otto Frost

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Emil Gustavsson

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Mats Jirstrand

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Johan Malmqvist

Chalmers, Industri- och materialvetenskap, Produktutveckling

Concurrent Engineering Research and Applications

1063-293X (ISSN)

Vol. 29 2 142-152







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