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Supporting Knowledg...
Supporting Knowledge Re-Use with Effective Searches of Related Engineering Documents - A Comparison of Search Engine and Natural Language-Based Processing Algorithms
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- Arnarsson, Ívar Örn, 1988 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Frost, Otto, 1990 (författare)
- Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik (FCC),Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC)
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- Gustavsson, Emil, 1987 (författare)
- Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik (FCC),Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC)
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- Stenholm, Daniel, 1989 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Jirstrand, Mats, 1968 (författare)
- Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik (FCC),Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC)
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- Malmqvist, Johan, 1964 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2019-07-26
- 2019
- Engelska.
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Ingår i: Proceedings of the International Conference on Engineering Design, ICED. - : Cambridge University Press (CUP). - 2220-4334 .- 2220-4342. ; 2019-August, s. 2597-2606
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)
Nyckelord
- Semantic data processing
- Natural Language Processing
- Machine learning
- Knowledge management
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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