Knowledge fragment enrichment using domain knowledge base

Jing Zhang; Honglei Zhuang; Yanglei Song; Jiawei Han; Yutao Zhang; Jie Tang; Juanzi Li

doi:10.1007/978-981-10-2993-6_24

Knowledge fragment enrichment using domain knowledge base

Jing Zhang, Honglei Zhuang, Yanglei Song, Jiawei Han, Yutao Zhang, Jie Tang, Juanzi Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Knowledge fragment enrichment aims to complete user input concept fragment by augmenting each concept with rich domain information. This is a widely studied problem in cognitive science, but has not been intensively investigated in computer science. In this paper, we formally define the problem of knowledge fragment enrichment in domain knowledge base and develop a probabilistic graphical model to tackle the problem. The proposed model is able to model the dependencies among concepts in the input knowledge fragment and also capture the probabilistic relationship between concepts and domain entities. We empirically evaluate the proposed model on two different genres of datasets: PubMed and NSFC. On both datasets, the proposed model significantly improves the accuracy of label prediction task by up to 3–9% (in terms of MAP) compared with several alternative enrichment methods.

Original language	English (US)
Title of host publication	Social Media Processing - 5th National Conference, SMP 2016, Proceedings
Editors	Hongfei Lin, Yuming Li, Guoxiong Xiang, Mingwen Wang
Publisher	Springer-Verlag Berlin Heidelberg
Pages	274-286
Number of pages	13
ISBN (Print)	9789811029929
DOIs	https://doi.org/10.1007/978-981-10-2993-6_24
State	Published - 2016
Event	5th National Conference on Social Media Processing, SMP 2016 - Nanchang, China Duration: Oct 29 2016 → Oct 30 2016

Publication series

Name	Communications in Computer and Information Science
Volume	669
ISSN (Print)	1865-0929

Other

Other	5th National Conference on Social Media Processing, SMP 2016
Country	China
City	Nanchang
Period	10/29/16 → 10/30/16

Keywords

Heterogeneous information networks
Hierarchical topic modeling
Semi-supervised labeling

ASJC Scopus subject areas

Computer Science(all)
Mathematics(all)

Access to Document

10.1007/978-981-10-2993-6_24

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Cite this

Zhang, J., Zhuang, H., Song, Y., Han, J., Zhang, Y., Tang, J., & Li, J. (2016). Knowledge fragment enrichment using domain knowledge base. In H. Lin, Y. Li, G. Xiang, & M. Wang (Eds.), Social Media Processing - 5th National Conference, SMP 2016, Proceedings (pp. 274-286). (Communications in Computer and Information Science; Vol. 669). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-981-10-2993-6_24

Knowledge fragment enrichment using domain knowledge base. / Zhang, Jing; Zhuang, Honglei; Song, Yanglei; Han, Jiawei; Zhang, Yutao; Tang, Jie; Li, Juanzi.

Social Media Processing - 5th National Conference, SMP 2016, Proceedings. ed. / Hongfei Lin; Yuming Li; Guoxiong Xiang; Mingwen Wang. Springer-Verlag Berlin Heidelberg, 2016. p. 274-286 (Communications in Computer and Information Science; Vol. 669).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, J, Zhuang, H, Song, Y, Han, J, Zhang, Y, Tang, J & Li, J 2016, Knowledge fragment enrichment using domain knowledge base. in H Lin, Y Li, G Xiang & M Wang (eds), Social Media Processing - 5th National Conference, SMP 2016, Proceedings. Communications in Computer and Information Science, vol. 669, Springer-Verlag Berlin Heidelberg, pp. 274-286, 5th National Conference on Social Media Processing, SMP 2016, Nanchang, China, 10/29/16. https://doi.org/10.1007/978-981-10-2993-6_24

Zhang J, Zhuang H, Song Y, Han J, Zhang Y, Tang J et al. Knowledge fragment enrichment using domain knowledge base. In Lin H, Li Y, Xiang G, Wang M, editors, Social Media Processing - 5th National Conference, SMP 2016, Proceedings. Springer-Verlag Berlin Heidelberg. 2016. p. 274-286. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-10-2993-6_24

Zhang, Jing ; Zhuang, Honglei ; Song, Yanglei ; Han, Jiawei ; Zhang, Yutao ; Tang, Jie ; Li, Juanzi. / Knowledge fragment enrichment using domain knowledge base. Social Media Processing - 5th National Conference, SMP 2016, Proceedings. editor / Hongfei Lin ; Yuming Li ; Guoxiong Xiang ; Mingwen Wang. Springer-Verlag Berlin Heidelberg, 2016. pp. 274-286 (Communications in Computer and Information Science).

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abstract = "Knowledge fragment enrichment aims to complete user input concept fragment by augmenting each concept with rich domain information. This is a widely studied problem in cognitive science, but has not been intensively investigated in computer science. In this paper, we formally define the problem of knowledge fragment enrichment in domain knowledge base and develop a probabilistic graphical model to tackle the problem. The proposed model is able to model the dependencies among concepts in the input knowledge fragment and also capture the probabilistic relationship between concepts and domain entities. We empirically evaluate the proposed model on two different genres of datasets: PubMed and NSFC. On both datasets, the proposed model significantly improves the accuracy of label prediction task by up to 3–9% (in terms of MAP) compared with several alternative enrichment methods.",

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AU - Li, Juanzi

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AB - Knowledge fragment enrichment aims to complete user input concept fragment by augmenting each concept with rich domain information. This is a widely studied problem in cognitive science, but has not been intensively investigated in computer science. In this paper, we formally define the problem of knowledge fragment enrichment in domain knowledge base and develop a probabilistic graphical model to tackle the problem. The proposed model is able to model the dependencies among concepts in the input knowledge fragment and also capture the probabilistic relationship between concepts and domain entities. We empirically evaluate the proposed model on two different genres of datasets: PubMed and NSFC. On both datasets, the proposed model significantly improves the accuracy of label prediction task by up to 3–9% (in terms of MAP) compared with several alternative enrichment methods.

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