International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Development of the Rule-based Smart Tourism Chatbot using Neo4J graph database

  • Kim, Dong-Hyun (Department of Telecommunication Engineering, Jeju National University) ;
  • Im, Hyeon-Su (Department of Telecommunication Engineering, Jeju National University) ;
  • Hyeon, Jong-Heon (Department of Telecommunication Engineering, Jeju National University) ;
  • Jwa, Jeong-Woo (Department of Telecommunication Engineering, Jeju National University)
  • Received : 2021.03.23
  • Accepted : 2021.04.04
  • Published : 2021.05.31
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Abstract

We have been developed the smart tourism app and the Instagram and YouTube contents to provide personalized tourism information and travel product information to individual tourists. In this paper, we develop a rule-based smart tourism chatbot with the khaiii (Kakao Hangul Analyzer III) morphological analyzer and Neo4J graph database. In the proposed chatbot system, we use a morpheme analyzer, a proper noun dictionary including tourist destination names, and a general noun dictionary including containing frequently used words in tourist information search to understand the intention of the user's question. The tourism knowledge base built using the Neo4J graph database provides adequate answers to tourists' questions. In this paper, the nodes of Neo4J are Area based on tourist destination address, Contents with property of tourist information, and Service including service attribute data frequently used for search. A Neo4J query is created based on the result of analyzing the intention of a tourist's question with the property of nodes and relationships in Neo4J database. An answer to the question is made by searching in the tourism knowledge base. In this paper, we create the tourism knowledge base using more than 1300 Jeju tourism information used in the smart tourism app. We plan to develop a multilingual smart tour chatbot using the named entity recognition (NER), intention classification using conditional random field(CRF), and transfer learning using the pretrained language models.

Keywords

Acknowledgement

This research was also supported by the 2020 scientific promotion program funded by Jeju National University.

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