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

被引：9

作者：

Arnarsson, Ivar Orn ^{[1
]}

Frost, Otto ^{[2
]}

Gustavsson, Emil ^{[2
]}

Jirstrand, Mats ^{[2
]}

Malmqvist, Johan ^{[1
]}

机构：

[1] Chalmers Univ Technol, Dept Ind & Mat Sci, Chalmersplatsen 4, S-41296 Gothenburg, Sweden

[2] Fraunhofer Chalmers Ctr, Gothenburg, Sweden

来源：

CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS | 2021年 / 29卷 / 02期

关键词：

natural language processing; document clustering; semantic data processing; knowledge management; engineering change request;

D O I：

10.1177/1063293X20982973

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：142 / 152

页数：11

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