Conversation intention perception based on knowledge base

Yi Zheng Chen; Hua Kang Li; Yi Liu

doi:10.1007/978-3-319-13186-3_1

Conversation intention perception based on knowledge base

Yi Zheng Chen^*, Hua Kang Li, Yi Liu

^*Corresponding author for this work

Shanghai Jiao Tong University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

Web Intelligence is gaining its growth in a rapid speed. The notion of wisdom, which is considered as the next paradigm shift of WI, has become a hot research topic in recent years. The basic application of wisdom is making a short conversation in an interactive and understandable way based on the huge web resources. However, current conversation system normally applies the recognition of semantic similarities in the prepared database, neglecting the true intention hiding in the expression. In this paper, we present a model based on the medical Q&A knowledge base to overcome this challenge. The knowledge base includes three parts: disease entity, medicine, properties. A simple graph path algorithm based on words direction and relation weight adjustment is used to realize conversation intention perception. The experimental results show that this method can effectively perceive types of intention. This method can also be applied in deep understanding of other intelligent systems such as classifications and text mining.

Original language	English
Title of host publication	Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
Subtitle of host publication	DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers
Editors	Wen-Chih Peng, Haixun Wang, Zhi-Hua Zhou, Tu Bao Ho, Vincent S. Tseng, Arbee L.P. Chen, James Bailey
Publisher	Springer Verlag
Pages	3-14
Number of pages	12
ISBN (Electronic)	9783319131856
DOIs	https://doi.org/10.1007/978-3-319-13186-3_1
Publication status	Published - 2014
Externally published	Yes
Event	International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China Duration: 13 May 2014 → 16 May 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8643
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
Country/Territory	Taiwan, Province of China
City	Tainan
Period	13/05/14 → 16/05/14

Keywords

Conversation system
Graph path
Intention perception
Knowledge base
Web Intelligence

Access to Document

10.1007/978-3-319-13186-3_1

Cite this

Chen, Y. Z., Li, H. K., & Liu, Y. (2014). Conversation intention perception based on knowledge base. In W.-C. Peng, H. Wang, Z.-H. Zhou, T. B. Ho, V. S. Tseng, A. L. P. Chen, & J. Bailey (Eds.), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers (pp. 3-14). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643). Springer Verlag. https://doi.org/10.1007/978-3-319-13186-3_1

Chen, Yi Zheng ; Li, Hua Kang ; Liu, Yi. / Conversation intention perception based on knowledge base. Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. editor / Wen-Chih Peng ; Haixun Wang ; Zhi-Hua Zhou ; Tu Bao Ho ; Vincent S. Tseng ; Arbee L.P. Chen ; James Bailey. Springer Verlag, 2014. pp. 3-14 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Conversation intention perception based on knowledge base",

abstract = "Web Intelligence is gaining its growth in a rapid speed. The notion of wisdom, which is considered as the next paradigm shift of WI, has become a hot research topic in recent years. The basic application of wisdom is making a short conversation in an interactive and understandable way based on the huge web resources. However, current conversation system normally applies the recognition of semantic similarities in the prepared database, neglecting the true intention hiding in the expression. In this paper, we present a model based on the medical Q&A knowledge base to overcome this challenge. The knowledge base includes three parts: disease entity, medicine, properties. A simple graph path algorithm based on words direction and relation weight adjustment is used to realize conversation intention perception. The experimental results show that this method can effectively perceive types of intention. This method can also be applied in deep understanding of other intelligent systems such as classifications and text mining.",

keywords = "Conversation system, Graph path, Intention perception, Knowledge base, Web Intelligence",

author = "Chen, {Yi Zheng} and Li, {Hua Kang} and Yi Liu",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 ; Conference date: 13-05-2014 Through 16-05-2014",

year = "2014",

doi = "10.1007/978-3-319-13186-3_1",

language = "English",

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

publisher = "Springer Verlag",

pages = "3--14",

editor = "Wen-Chih Peng and Haixun Wang and Zhi-Hua Zhou and Ho, {Tu Bao} and Tseng, {Vincent S.} and Chen, {Arbee L.P.} and James Bailey",

booktitle = "Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops",

}

Chen, YZ, Li, HK & Liu, Y 2014, Conversation intention perception based on knowledge base. in W-C Peng, H Wang, Z-H Zhou, TB Ho, VS Tseng, ALP Chen & J Bailey (eds), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8643, Springer Verlag, pp. 3-14, International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, Tainan, Taiwan, Province of China, 13/05/14. https://doi.org/10.1007/978-3-319-13186-3_1

Conversation intention perception based on knowledge base. / Chen, Yi Zheng; Li, Hua Kang; Liu, Yi.
Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. ed. / Wen-Chih Peng; Haixun Wang; Zhi-Hua Zhou; Tu Bao Ho; Vincent S. Tseng; Arbee L.P. Chen; James Bailey. Springer Verlag, 2014. p. 3-14 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Conversation intention perception based on knowledge base

AU - Chen, Yi Zheng

AU - Li, Hua Kang

AU - Liu, Yi

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - Web Intelligence is gaining its growth in a rapid speed. The notion of wisdom, which is considered as the next paradigm shift of WI, has become a hot research topic in recent years. The basic application of wisdom is making a short conversation in an interactive and understandable way based on the huge web resources. However, current conversation system normally applies the recognition of semantic similarities in the prepared database, neglecting the true intention hiding in the expression. In this paper, we present a model based on the medical Q&A knowledge base to overcome this challenge. The knowledge base includes three parts: disease entity, medicine, properties. A simple graph path algorithm based on words direction and relation weight adjustment is used to realize conversation intention perception. The experimental results show that this method can effectively perceive types of intention. This method can also be applied in deep understanding of other intelligent systems such as classifications and text mining.

AB - Web Intelligence is gaining its growth in a rapid speed. The notion of wisdom, which is considered as the next paradigm shift of WI, has become a hot research topic in recent years. The basic application of wisdom is making a short conversation in an interactive and understandable way based on the huge web resources. However, current conversation system normally applies the recognition of semantic similarities in the prepared database, neglecting the true intention hiding in the expression. In this paper, we present a model based on the medical Q&A knowledge base to overcome this challenge. The knowledge base includes three parts: disease entity, medicine, properties. A simple graph path algorithm based on words direction and relation weight adjustment is used to realize conversation intention perception. The experimental results show that this method can effectively perceive types of intention. This method can also be applied in deep understanding of other intelligent systems such as classifications and text mining.

KW - Conversation system

KW - Graph path

KW - Intention perception

KW - Knowledge base

KW - Web Intelligence

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U2 - 10.1007/978-3-319-13186-3_1

DO - 10.1007/978-3-319-13186-3_1

M3 - Conference Proceeding

AN - SCOPUS:84915815371

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

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BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops

A2 - Peng, Wen-Chih

A2 - Wang, Haixun

A2 - Zhou, Zhi-Hua

A2 - Ho, Tu Bao

A2 - Tseng, Vincent S.

A2 - Chen, Arbee L.P.

A2 - Bailey, James

PB - Springer Verlag

T2 - International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014

Y2 - 13 May 2014 through 16 May 2014

ER -

Chen YZ, Li HK, Liu Y. Conversation intention perception based on knowledge base. In Peng WC, Wang H, Zhou ZH, Ho TB, Tseng VS, Chen ALP, Bailey J, editors, Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. Springer Verlag. 2014. p. 3-14. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-13186-3_1

Conversation intention perception based on knowledge base

Abstract

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Conference

Keywords

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