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research-article

A data-driven text mining and self-learning semantic network analysis for design knowledge retrieval

[+] Author and Article Information
Feng Shi

PhD Student, Engineering Design Group, Dyson School of Design Engineering, Imperial College London, South Kensington, London, SW7 1NA
f.shi14@imperial.ac.uk

Liuqing Chen

PhD Student, Engineering Design Group, Dyson School of Design Engineering, Imperial College London, South Kensington, London, SW7 1NA
l.chen15@imperial.ac.uk

Ji Han

PhD Student, Engineering Design Group, Dyson School of Design Engineering, Imperial College London, South Kensington, London, SW7 1NA
j.han14@imperial.ac.uk

Peter R.N Childs

Professor, Head of the School, Fellow of ASME, Dyson School of Design Engineering, Imperial College London, South Kensington, London, SW7 1NA
p.childs@imperial.ac.uk

1Corresponding author.

ASME doi:10.1115/1.4037649 History: Received February 10, 2017; Revised August 12, 2017

Abstract

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. While, ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. In this paper, we propose a data-driven ontology method consisting of semantic network construction and subsequent network analysis for design information retrieval. In the semantic network construction stage, an unsupervised learning algorithm combined with a web crawler is developed to automatically construct a semantic network by conducting simplified NLP and itemset mining techniques on massive raw textual data through both semantic and statistical levels. In the semantic network analysis stage, a new approach with two different retrieval behaviours is proposed by modelling probability and velocity layers on the semantic network. An engineering design case study shows the effectiveness of the method for retrieving useful and relevant knowledge concepts and providing feasible design solutions. The result also shows significant different retrieval behaviours of the probability layer and velocity layer, which indicates the potential capacity of the method in satisfying the various demands for different types of knowledge in engineering design activities.

Copyright (c) 2017 by ASME
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