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Exploiting a Large-scale Knowledge Graph for Question Generation in Food Preference Interview Systems

Authors:

Jie Zeng,

Yukiko I. NakanoAuthors Info & Claims

IUI '20 Companion: Companion Proceedings of the 25th International Conference on Intelligent User Interfaces

Pages 53 - 54

https://doi.org/10.1145/3379336.3381504

Published: 17 March 2020 Publication History

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Abstract

This paper presents a dialogue system that acquires user's food preference through a conversation. First, we proposed a method for selecting relevant topics and generating questions based on Freebase, a large-scale knowledge graph. To select relevant topics, using the Wikipedia corpus, we created a topic-embedding model that represents the correlation among topics. For missing entities in Freebase, knowledge completion was applied using knowledge graph embedding. We incorporated these functions into a dialogue system and conducted a user study. The results reveal that the proposed dialogue system more efficiently elicited words related to food and common nouns, and these words were highly correlated in a word embedding space.

References

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Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems. 2787--2795.

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[2]

Kyusong Lee, Paul Hongsuck Seo, Junhwi Choi, Sangjun Koo, and Gary Geunbae Lee. 2015. Conversational knowledge teaching agent that uses a knowledge base. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 139--143.

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Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.

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Cited By

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Singla AKhanna RKaur MKelm KZaiane ORosenfelt CBui TRezaei NNicholas DReformat MMajnemer AOgourtsova TBolduc F(2024)Developing a Chatbot to Support Individuals With Neurodevelopmental Disorders: TutorialJournal of Medical Internet Research10.2196/5018226(e50182)Online publication date: 18-Jun-2024
https://doi.org/10.2196/50182
Abu-Salih BAL-Qurishi MAlweshah MAL-Smadi MAlfayez RSaadeh H(2023)Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunitiesJournal of Big Data10.1186/s40537-023-00774-910:1Online publication date: 28-May-2023
https://doi.org/10.1186/s40537-023-00774-9
Komatani KFujioka YNakashima KHayashi KNakano MHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)Knowledge Graph Completion-based Question Selection for Acquiring Domain Knowledge through DialoguesProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450653(531-541)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450653

Index Terms

Exploiting a Large-scale Knowledge Graph for Question Generation in Food Preference Interview Systems
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Natural language interfaces

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Published In

IUI '20 Companion: Companion Proceedings of the 25th International Conference on Intelligent User Interfaces

March 2020

153 pages

ISBN:9781450375139

DOI:10.1145/3379336

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 17 March 2020

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IUI '20: 25th International Conference on Intelligent User Interfaces

March 17 - 20, 2020

Cagliari, Italy

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Singla AKhanna RKaur MKelm KZaiane ORosenfelt CBui TRezaei NNicholas DReformat MMajnemer AOgourtsova TBolduc F(2024)Developing a Chatbot to Support Individuals With Neurodevelopmental Disorders: TutorialJournal of Medical Internet Research10.2196/5018226(e50182)Online publication date: 18-Jun-2024
https://doi.org/10.2196/50182
Abu-Salih BAL-Qurishi MAlweshah MAL-Smadi MAlfayez RSaadeh H(2023)Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunitiesJournal of Big Data10.1186/s40537-023-00774-910:1Online publication date: 28-May-2023
https://doi.org/10.1186/s40537-023-00774-9
Komatani KFujioka YNakashima KHayashi KNakano MHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)Knowledge Graph Completion-based Question Selection for Acquiring Domain Knowledge through DialoguesProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450653(531-541)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450653

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Towards Informative and Diverse Dialogue Systems Over Hierarchical Crowd Intelligence Knowledge Graph

Universal Knowledge Graph Embeddings

Explainable Knowledge Reasoning Framework Using Multiple Knowledge Graph Embedding

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Towards Informative and Diverse Dialogue Systems Over Hierarchical Crowd Intelligence Knowledge Graph

Universal Knowledge Graph Embeddings

Explainable Knowledge Reasoning Framework Using Multiple Knowledge Graph Embedding

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