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International Conference on Information and Software Technologies

ICIST 2018: Information and Software Technologies pp 14–28Cite as

Metis: A Scalable Natural-Language-Based Intelligent Personal Assistant for Maritime Services

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 920))

Abstract

We implement an intelligent personal conversational assistant, communicating in natural language and designed specifically for the maritime industry. A multi-stage message analysis is performed, first classifying the topic of the request and finally applying special parsers to extract the parameters needed to execute the task. Our system is scalable and robust, employing generic and efficient algorithms. Our contributions are manifold. First, we present a complex and multi-level natural-language-processing-based system, focused particularly on the maritime domain and incorporating expert knowledge of the field. Next, we introduce a series of algorithms that can extract deep information using the syntactic structure of the message. Lastly, we implement and evaluate our approach, testing and proving our system’s effectiveness and efficiency.

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Author information

Authors and Affiliations

  1. METIS Cyberspace Technology SA, 4 Sachtouri & 2 Posidonos Ave., 17674, Kallithea, Greece

    Nikolaos Gkanatsios, Konstantina Mermikli & Serafeim Katsikas

Authors
  1. Nikolaos Gkanatsios
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  2. Konstantina Mermikli
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  3. Serafeim Katsikas
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Corresponding author

Correspondence to Nikolaos Gkanatsios .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Giedrė Vasiljevienė

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Gkanatsios, N., Mermikli, K., Katsikas, S. (2018). Metis: A Scalable Natural-Language-Based Intelligent Personal Assistant for Maritime Services. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-319-99972-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-99972-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99971-5

  • Online ISBN: 978-3-319-99972-2

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