Improving engineering information retrieval by combining tf-idf and product structure classification

David Jones, Jason Matthews, Yifen Xie, James Gopsill, Martin Dotter, Nicolas Chanchevrier, Ben Hicks

Research output: Contribution to conferencePaper

Abstract

Engineering Information Management (EIM) and Information Retrieval (IR) systems are central to the day to day running of large engineering organisations. The capture, interrogation, retrieval and presentation of information from design to disposal is considered to be a key enabler for greater efficiency and decision making and in turn improved productivity, profitability and competitiveness. This paper presents a contribution to the field of engineering IR through combining TF-IDF with classification against the product structure. The results of this initial investigation show that Precision, Recall and F1-Scores can be improved depending on the method of results integration and thus tailored to the search system and context.
Original languageEnglish
Pages41 - 50
Number of pages10
Publication statusPublished - 24 Aug 2017
EventInternational Conference on Engineering Design - University of British Columbia, Vancouver, Canada
Duration: 21 Aug 201625 Aug 2016
Conference number: 21
http://iced17.org

Conference

ConferenceInternational Conference on Engineering Design
Abbreviated titleICED 2017
CountryCanada
CityVancouver
Period21/08/1625/08/16
Internet address

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  • Cite this

    Jones, D., Matthews, J., Xie, Y., Gopsill, J., Dotter, M., Chanchevrier, N., & Hicks, B. (2017). Improving engineering information retrieval by combining tf-idf and product structure classification. 41 - 50. Paper presented at International Conference on Engineering Design, Vancouver, Canada. https://jamesgopsill.github.io/Publications/papers/conference/iced2017/iced2017c.pdf