Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs

Jiahui Hu; Zhisheng Huang; Wei Chen; Pei Lou; Wanqing Zhao; Kuanda Yao; An Fang

doi:10.1145/3500931.3500954

Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs

Jiahui Hu, Zhisheng Huang, Wei Chen, Pei Lou, Wanqing Zhao, Kuanda Yao, An Fang

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Nutrition is closely associated with public health, and more and more studies have demonstrated that there are many correlations between the gut microbiota and nutritional disorders. These studies constitute the basic source of medical knowledge. However, discovering knowledge from massive amounts of data is not easy only by manpower. This study aims at discovering the relationships between nutritional disorders and gut microbiota from the biomedical literatures with knowledge graph. In this study, literature content information from massive data is automatically and instantly extracted using SPARQL query and logic reasoning technologies. Then, an annotated corpus is obtained with the categorization and definition of relationships between nutritional disorders and gut microbiota. Thus, a knowledge graph of semantic relationship between nutritional disorders and gut microbiota is constructed, and semantic relationships are discovered, verifying the feasibility and high efficiency of relationships discovery with knowledge graphs. Moreover, the analysis of evidence case shows that it is necessary and valuable to use the importance of evidence to exclude conflicting conclusions.

Original language	English
Title of host publication	ISAIMS 2021
Subtitle of host publication	Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences
Publisher	Association for Computing Machinery
Pages	126-131
Number of pages	6
ISBN (Electronic)	9781450395588
DOIs	https://doi.org/10.1145/3500931.3500954
Publication status	Published - Oct 2021
Event	2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021 - Virtual, Online, China Duration: 29 Oct 2021 → 31 Oct 2021

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021
Country/Territory	China
City	Virtual, Online
Period	29/10/21 → 31/10/21

Bibliographical note

Publisher Copyright:
© 2021 ACM.

Funding

&iis research has been funded by the Science and Technology Innovation 2030-Major Project (2020AAA0104902), the National Social Science Foundation of China (Grant No. 21CTQ016), and the National Natural Science Foundation of China (Grant No. 72074222).

Funders	Funder number
National Natural Science Foundation of China	72074222
National Office for Philosophy and Social Sciences	21CTQ016

Keywords

Gut microbiota
Knowledge graph
Nutritional Disorders
Relationship Discovery

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3500931.3500954

Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge GraphsFinal published version, 1.38 MBLicence: Article 25fa Dutch Copyright Act

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Cite this

Hu, J., Huang, Z., Chen, W., Lou, P., Zhao, W., Yao, K., & Fang, A. (2021). Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs. In ISAIMS 2021: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences (pp. 126-131). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3500931.3500954

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abstract = "Nutrition is closely associated with public health, and more and more studies have demonstrated that there are many correlations between the gut microbiota and nutritional disorders. These studies constitute the basic source of medical knowledge. However, discovering knowledge from massive amounts of data is not easy only by manpower. This study aims at discovering the relationships between nutritional disorders and gut microbiota from the biomedical literatures with knowledge graph. In this study, literature content information from massive data is automatically and instantly extracted using SPARQL query and logic reasoning technologies. Then, an annotated corpus is obtained with the categorization and definition of relationships between nutritional disorders and gut microbiota. Thus, a knowledge graph of semantic relationship between nutritional disorders and gut microbiota is constructed, and semantic relationships are discovered, verifying the feasibility and high efficiency of relationships discovery with knowledge graphs. Moreover, the analysis of evidence case shows that it is necessary and valuable to use the importance of evidence to exclude conflicting conclusions.",

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author = "Jiahui Hu and Zhisheng Huang and Wei Chen and Pei Lou and Wanqing Zhao and Kuanda Yao and An Fang",

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year = "2021",

month = oct,

doi = "10.1145/3500931.3500954",

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Hu, J, Huang, Z, Chen, W, Lou, P, Zhao, W, Yao, K & Fang, A 2021, Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs. in ISAIMS 2021: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 126-131, 2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021, Virtual, Online, China, 29/10/21. https://doi.org/10.1145/3500931.3500954

Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs. / Hu, Jiahui; Huang, Zhisheng; Chen, Wei et al.
ISAIMS 2021: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences. Association for Computing Machinery, 2021. p. 126-131 (ACM International Conference Proceeding Series).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Hu, Jiahui

AU - Huang, Zhisheng

AU - Chen, Wei

AU - Lou, Pei

AU - Zhao, Wanqing

AU - Yao, Kuanda

AU - Fang, An

PY - 2021/10

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N2 - Nutrition is closely associated with public health, and more and more studies have demonstrated that there are many correlations between the gut microbiota and nutritional disorders. These studies constitute the basic source of medical knowledge. However, discovering knowledge from massive amounts of data is not easy only by manpower. This study aims at discovering the relationships between nutritional disorders and gut microbiota from the biomedical literatures with knowledge graph. In this study, literature content information from massive data is automatically and instantly extracted using SPARQL query and logic reasoning technologies. Then, an annotated corpus is obtained with the categorization and definition of relationships between nutritional disorders and gut microbiota. Thus, a knowledge graph of semantic relationship between nutritional disorders and gut microbiota is constructed, and semantic relationships are discovered, verifying the feasibility and high efficiency of relationships discovery with knowledge graphs. Moreover, the analysis of evidence case shows that it is necessary and valuable to use the importance of evidence to exclude conflicting conclusions.

AB - Nutrition is closely associated with public health, and more and more studies have demonstrated that there are many correlations between the gut microbiota and nutritional disorders. These studies constitute the basic source of medical knowledge. However, discovering knowledge from massive amounts of data is not easy only by manpower. This study aims at discovering the relationships between nutritional disorders and gut microbiota from the biomedical literatures with knowledge graph. In this study, literature content information from massive data is automatically and instantly extracted using SPARQL query and logic reasoning technologies. Then, an annotated corpus is obtained with the categorization and definition of relationships between nutritional disorders and gut microbiota. Thus, a knowledge graph of semantic relationship between nutritional disorders and gut microbiota is constructed, and semantic relationships are discovered, verifying the feasibility and high efficiency of relationships discovery with knowledge graphs. Moreover, the analysis of evidence case shows that it is necessary and valuable to use the importance of evidence to exclude conflicting conclusions.

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KW - Knowledge graph

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Hu J, Huang Z, Chen W, Lou P, Zhao W, Yao K et al. Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs. In ISAIMS 2021: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences. Association for Computing Machinery. 2021. p. 126-131. (ACM International Conference Proceeding Series). doi: 10.1145/3500931.3500954

Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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