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Ontology Development Through Concept Map and Text Analytics: The Case of Automotive Safety Ontology

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Book cover Natural Language Processing and Information Systems (NLDB 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10859))

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Abstract

Ontology development is an expensive and time-consuming process. The development of real-world organizational ontology-based knowledge management systems is still in early stages. Some existing ontologies with simple tuples and properties are not designed for domain specific requirement, or does not utilize existing knowledge from organizational database or documents. Here we propose our concept map approach to first semi-automatically create a detailed level entities/concepts as a keyword list by applying natural language processing, including word dependency and POS tagging. Then this list can be used to extract entities/concepts for the same domain. This approach is applied to automotive safety domain. The results are further mapped to existing ontology and aggregated to form a concept map. We implement our approach in KNIME with Stanford NLP parser and generate a concept map from automotive safety complaint dataset. The final results expand the existing ontology, and also bridge the gap between ontology and real-world organization ontology-based knowledge management systems.

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Notes

  1. 1.

    Dataset from NHTSA link: https://www-odi.nhtsa.dot.gov/downloads/.

  2. 2.

    DBpedia automobile class link: http://dbpedia.org/ontology/Automobile.

  3. 3.

    For more detail about KNIME, please visit: https://www.knime.com/.

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Author information

Authors and Affiliations

  1. J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, 30302, USA

    Zirun Qi

  2. Center for Data Science and Big Data Analytics, Department of Decision, and Information Sciences, Oakland University, Rochester, MI, 48309, USA

    Vijayan Sugumaran

Authors
  1. Zirun Qi
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  2. Vijayan Sugumaran
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Correspondence to Zirun Qi .

Editor information

Editors and Affiliations

  1. Université de Franche-Comté, Besançon, France

    Max Silberztein

  2. Conservatoire National des Arts et Métiers, Paris, France

    Faten Atigui

  3. Conservatoire National des Arts et Métiers, Paris, France

    Elena Kornyshova

  4. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  5. University of Salford, Manchester, United Kingdom

    Farid Meziane

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Qi, Z., Sugumaran, V. (2018). Ontology Development Through Concept Map and Text Analytics: The Case of Automotive Safety Ontology. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-91947-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91946-1

  • Online ISBN: 978-3-319-91947-8

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