A correlation computing method for integrating passengers and services in semantic anticipation

Motoki Yokoyama, Yasushi Kiyoki, Tetsuya Mita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

New information-provision focusing on individual passengers is expected in a railway environment. Our method realizes multiple semantic spaces, which are selected according to passenger's contexts. By using correlation metrics for the causal interaction of different semantic spaces, this method anticipates the passenger's needs and generates a ranking of services and facilities. Experimental study confirms that the ranking of passenger requirements for services and facilities would change appropriately in response to the causal interaction of the semantic space. The experimental results show the feasibility and applicability of this method.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXX
EditorsTatiana Endrjukaite, Hannu Jaakkola, Alexander Dudko, Yasushi Kiyoki, Bernhard Thalheim, Naofumi Yoshida
PublisherIOS Press
Pages435-448
Number of pages14
ISBN (Electronic)9781614999324
DOIs
Publication statusPublished - 2019 Jan 1

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume312
ISSN (Print)0922-6389

Keywords

  • Context Awareness
  • Information Modeling
  • Information retrieval
  • Semantic Computing

ASJC Scopus subject areas

  • Artificial Intelligence

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