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. 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. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval



    Erschienen in:

    Erscheinungsdatum :

    2017




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    50.15 Konstruktionslehre / 52.20 Antriebstechnik, Getriebelehre / 52.15 Maschinenelemente, Maschinenbaugruppen
    Lokalklassifikation TIB:    770/5315/5330



    A Speech-Text Retrieval System Using Semantic Data of the English/Japanese Conversational Texts

    Kawaguchi, E. / Lee, M. / Nozaki, K. et al. | British Library Conference Proceedings | 1994


    Full Text Retrieval in Imaging & Information Filtering

    Scholtes, J. C. / IMC | British Library Conference Proceedings | 1994