research-article

Food safety Knowledge Graph and Question Answering System

Authors:

Liang ZhaoAuthors Info & Claims

ICIT '19: Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City

Pages 559 - 564

https://doi.org/10.1145/3377170.3377260

Published: 20 March 2020 Publication History

Abstract

The issue of food safety in recent years has always been the focus of public opinion. Every time there are unqualified foods, it will cause widespread panic and rumor spread, which has a great impact on social stability. Therefore, this paper crawled the data of unqualified foods officially released in recent years from the network, and designed the extraction algorithm of food general entities, food domain entities and relationships between entities for these data. The extracted entity pairs were stored in the gStore database. In order to solve the problem of association of knowledge in knowledge graph, this paper also designed the food safety ontology which organized the concepts, classifications and relationships about food production and food inspection. Finally, this paper also built an intelligent question answering system by means of gStore's http service to help person grasp the unqualified food information through natural language.

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Cited By

Fu CPan XWu JCai JHuang Zvan Harmelen FZhao WJiang XHe T(2025)KG4NH: A Comprehensive Knowledge Graph for Question Answering in Dietary Nutrition and Human HealthIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.333835629:3(1793-1804)Online publication date: Mar-2025
https://doi.org/10.1109/JBHI.2023.3338356
De Carolis BLofrese DDi Pierro DFerilli S(2025)NutriWell: An Explainable Ontology-Based FoodAI Service for Nutrition and Health ManagementAIxIA 2024 – Advances in Artificial Intelligence10.1007/978-3-031-80607-0_11(133-146)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-80607-0_11
Xiong STian WSi HZhang GShi L(2024)A Survey of the Applications of Text Mining for the Food DomainAlgorithms10.3390/a1705017617:5(176)Online publication date: 25-Apr-2024
https://doi.org/10.3390/a17050176
Show More Cited By

Index Terms

Food safety Knowledge Graph and Question Answering System
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Other conferences

ICIT '19: Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City

December 2019

601 pages

ISBN:9781450376631

DOI:10.1145/3377170

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Shanghai Jiao Tong University: Shanghai Jiao Tong University
The Hong Kong Polytechnic: The Hong Kong Polytechnic University
University of Malaya: University of Malaya

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 March 2020

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Funding Sources

National Key Research and Development Project
Fundamental Research Funds for the Central Universities of HuaZhong Agricultural University

Conference

ICIT 2019

ICIT 2019: IoT and Smart City

December 20 - 23, 2019

Shanghai, China

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Cited By

Fu CPan XWu JCai JHuang Zvan Harmelen FZhao WJiang XHe T(2025)KG4NH: A Comprehensive Knowledge Graph for Question Answering in Dietary Nutrition and Human HealthIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.333835629:3(1793-1804)Online publication date: Mar-2025
https://doi.org/10.1109/JBHI.2023.3338356
De Carolis BLofrese DDi Pierro DFerilli S(2025)NutriWell: An Explainable Ontology-Based FoodAI Service for Nutrition and Health ManagementAIxIA 2024 – Advances in Artificial Intelligence10.1007/978-3-031-80607-0_11(133-146)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-80607-0_11
Xiong STian WSi HZhang GShi L(2024)A Survey of the Applications of Text Mining for the Food DomainAlgorithms10.3390/a1705017617:5(176)Online publication date: 25-Apr-2024
https://doi.org/10.3390/a17050176
Tzitzikas YMarketakis YFafalios PKritsotaki AAxaridou AMountantonakis MTheodoridou MBekiari C(2024)Ontologies and Case StudiesEFSA Supporting Publications10.2903/sp.efsa.2024.EN-912021:12Online publication date: Dec-2024
https://doi.org/10.2903/sp.efsa.2024.EN-9120
Solano PHerrera VAbril-Ulloa VEspinoza-Mejía M(2024)Preventing Diabetes: Substituting Processed Foods and Nutritional Chatbot AssistanceInternational Conference on Applied Technologies10.1007/978-3-031-58953-9_18(226-240)Online publication date: 29-May-2024
https://doi.org/10.1007/978-3-031-58953-9_18
Yang LGuo ZXu XKang HLai JLi J(2023)An Online Multimodal Food Data Exploration Platform for Specific Population Health: Development Study (Preprint)JMIR Formative Research10.2196/55088Online publication date: 2-Dec-2023
https://doi.org/10.2196/55088
Xiong STian WBatra VFan XXi LLiu HLiu L(2023)Food safety news events classification via a hierarchical transformer modelHeliyon10.1016/j.heliyon.2023.e17806(e17806)Online publication date: Jun-2023
https://doi.org/10.1016/j.heliyon.2023.e17806
Guo YChen YWei WLi H(2023)Research on Food Recommendation Method Based on Knowledge GraphComputer Science and Education10.1007/978-981-99-2443-1_45(521-533)Online publication date: 14-May-2023
https://doi.org/10.1007/978-981-99-2443-1_45
Hu DWeng CWang RSong XQin L(2023)Construction Method of National Food Safety Standard OntologyGreen, Pervasive, and Cloud Computing10.1007/978-3-031-26118-3_4(50-66)Online publication date: 1-Feb-2023
https://doi.org/10.1007/978-3-031-26118-3_4
Wang DLi HWang WQiao L(2022)Cross-Modal Knowledge Graph Construction for Multiple Food AdditivesProceedings of 2022 Chinese Intelligent Systems Conference10.1007/978-981-19-6226-4_80(839-847)Online publication date: 24-Sep-2022
https://doi.org/10.1007/978-981-19-6226-4_80

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