Constructing disease-centric knowledge graphs: A case study for depression (short version)

Zhisheng Huang; Jie Yang; Frank van Harmelen; Qing Hu

doi:10.1007/978-3-319-59758-4_5

Constructing disease-centric knowledge graphs: A case study for depression (short version)

Zhisheng Huang^*, Jie Yang, Frank van Harmelen, Qing Hu

^*Corresponding author for this work

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

Abstract

In this paper we show how we used multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries.

Original language	English
Title of host publication	Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings
Publisher	Springer/Verlag
Pages	48-52
Number of pages	5
Volume	10259 LNAI
ISBN (Print)	9783319597577
DOIs	https://doi.org/10.1007/978-3-319-59758-4_5
Publication status	Published - 2017
Event	16th Conference on Artificial Intelligence in Medicine, AIME 2017 - Vienna, Austria Duration: 21 Jun 2017 → 24 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10259 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Conference on Artificial Intelligence in Medicine, AIME 2017
Country/Territory	Austria
City	Vienna
Period	21/06/17 → 24/06/17

UN SDGs

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

Access to Document

10.1007/978-3-319-59758-4_5

Handle.net

Persistent link

Cite this

Huang, Z., Yang, J., van Harmelen, F., & Hu, Q. (2017). Constructing disease-centric knowledge graphs: A case study for depression (short version). In Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings (Vol. 10259 LNAI, pp. 48-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10259 LNAI). Springer/Verlag. https://doi.org/10.1007/978-3-319-59758-4_5

Huang, Zhisheng ; Yang, Jie ; van Harmelen, Frank et al. / Constructing disease-centric knowledge graphs : A case study for depression (short version). Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. Vol. 10259 LNAI Springer/Verlag, 2017. pp. 48-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{db25399349794a8f86dce8ed0ec8ac69,

title = "Constructing disease-centric knowledge graphs: A case study for depression (short version)",

abstract = "In this paper we show how we used multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries.",

author = "Zhisheng Huang and Jie Yang and {van Harmelen}, Frank and Qing Hu",

year = "2017",

doi = "10.1007/978-3-319-59758-4_5",

language = "English",

isbn = "9783319597577",

volume = "10259 LNAI",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer/Verlag",

pages = "48--52",

booktitle = "Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings",

note = "16th Conference on Artificial Intelligence in Medicine, AIME 2017 ; Conference date: 21-06-2017 Through 24-06-2017",

}

Huang, Z, Yang, J, van Harmelen, F & Hu, Q 2017, Constructing disease-centric knowledge graphs: A case study for depression (short version). in Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. vol. 10259 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10259 LNAI, Springer/Verlag, pp. 48-52, 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, 21/06/17. https://doi.org/10.1007/978-3-319-59758-4_5

Constructing disease-centric knowledge graphs: A case study for depression (short version). / Huang, Zhisheng; Yang, Jie; van Harmelen, Frank et al.
Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. Vol. 10259 LNAI Springer/Verlag, 2017. p. 48-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10259 LNAI).

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

TY - GEN

T1 - Constructing disease-centric knowledge graphs

T2 - 16th Conference on Artificial Intelligence in Medicine, AIME 2017

AU - Huang, Zhisheng

AU - Yang, Jie

AU - van Harmelen, Frank

AU - Hu, Qing

PY - 2017

Y1 - 2017

N2 - In this paper we show how we used multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries.

AB - In this paper we show how we used multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries.

UR - http://www.scopus.com/inward/record.url?scp=85021640874&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85021640874&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-59758-4_5

DO - 10.1007/978-3-319-59758-4_5

M3 - Conference contribution

AN - SCOPUS:85021640874

SN - 9783319597577

VL - 10259 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 48

EP - 52

BT - Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings

PB - Springer/Verlag

Y2 - 21 June 2017 through 24 June 2017

ER -

Huang Z, Yang J, van Harmelen F, Hu Q. Constructing disease-centric knowledge graphs: A case study for depression (short version). In Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. Vol. 10259 LNAI. Springer/Verlag. 2017. p. 48-52. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59758-4_5

Constructing disease-centric knowledge graphs: A case study for depression (short version)

Abstract

Publication series

Conference

UN SDGs

Access to Document

Handle.net

Other files and links

Fingerprint

Cite this