research-article

Full-text based context-rich heterogeneous network mining approach for citation recommendation

Authors:

Liangcai GaoAuthors Info & Claims

JCDL '14: Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries

Pages 361 - 370

Published: 08 September 2014 Publication History

Abstract

Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper₁ cites paper₂ (or author₁ cites author₂), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance.

References

[1]

Q. He, J. Pei, D. Kifer, P. Mitra, and L. Giles, "Context-aware citation recommendation," in Proceedings of the 19th international conference on World wide web. ACM, 2010, pp. 421--430.

Digital Library

[2]

Q. He, D. Kifer, J. Pei, P. Mitra, and C. L. Giles, "Citation recommendation without author supervision," in Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011, pp. 755--764.

Digital Library

[3]

N. Lao and W. W. Cohen, "Relational retrieval using a combination of path-constrained random walks," Machine learning, vol. 81, no. 1, pp. 53--67, 2010.

Digital Library

[4]

X. Liu, J. Zhang, and C. Guo, "Full-text citation analysis: A new method to enhance scholarly networks," Journal of the American Society for Information Science and Technology, vol. 64, no. 9, pp. 1852--1863, 2013.

[5]

D. Ramage, D. Hall, R. Nallapati, and C. D. Manning, "Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora," in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1. Association for Computational Linguistics, 2009, pp. 248--256.

Digital Library

[6]

Y. Sun, J. Han, X. Yan, P. S. Yu, and T. Wu, "PathSim: Meta path-based top-k similarity search in heterogeneous information networks," in Proc. 2011 Int. Conf. Very Large Data Bases (VLDB'11), Seattle, WA, 2011, pp. 992--1003.

[7]

X. Liu, J. Zhang, and C. Guo, "Full-text citation analysis: enhancing bibliometric and scientific publication ranking," in Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2012, pp. 1975--1979.

Digital Library

[8]

Y. Ding, X. Liu, C. Guo, and B. Cronin, "The distribution of references across texts: Some implications for citation analysis," Journal of Informetrics, vol. 7, no. 3, pp. 583--592, 2013.

[9]

C. Guo, J. Zhang, and X. Liu, "Scientific metadata quality enhancement for scholarly publications," in iConference, 2012, pp. 777--780.

[10]

D. Shotton, "Cito, the citation typing ontology, and its use for annotation of reference lists and visualization of citation networks," in Bio-Ontologies 2009 Special Interest Group meeting at ISMB, 2009.

[11]

M. Dabrowski, M. Synak, and S. R. Kruk, "Bibliographic ontology," in Semantic digital libraries. Springer, 2009, pp. 103--122.

[12]

D. C. M. Initiative et al., "Dublin core metadata element set, version 1.1," 2008.

[13]

Y. Ding, X. Liu, C. Guo, and B. Cronin, "The distribution of references across texts: Some implications for citation analysis," Journal of Informetrics, vol. 7, no. 3, pp. 583--592, 2013.

[14]

X. Wan and F. Liu, "Are all literature citations equally important? automatic citation strength estimation and its applications," Journal of the Association for Information Science and Technology, 2014.

Digital Library

[15]

K. Chandrasekaran, S. Gauch, P. Lakkaraju, and H. P. Luong, "Concept-based document recommendations for citeseer authors," in Adaptive Hypermedia and Adaptive Web-Based Systems. Springer, 2008, pp. 83--92.

Digital Library

[16]

X. Yin, J. X. Huang, and Z. Li, "Mining and modeling linkage information from citation context for improving biomedical literature retrieval," Information Processing and Management, vol. 47, no. 1, pp. 53--67, 2011.

Digital Library

[17]

H. Xia, J. Li, J. Tang, and M.-F. Moens, "Plink-lda: Using link as prior information in topic modeling," in Database Systems for Advanced Applications. Springer, 2012, pp. 213--227.

Digital Library

[18]

X. Shi, J. Leskovec, and D. A. McFarland, "Citing for high impact," in Proceedings of the 10th annual joint conference on Digital libraries. ACM, 2010, pp. 49--58.

Digital Library

[19]

D. Walker, H. Xie, K.-K. Yan, and S. Maslov, "Ranking scientific publications using a model of network traffic," Journal of Statistical Mechanics: Theory and Experiment, vol. 2007, no. 06, p. P06010, 2007.

[20]

L. Dietz, S. Bickel, and T. Scheffer, "Unsupervised prediction of citation influences," in Proceedings of the 24th international conference on Machine learning. ACM, 2007, pp. 233--240.

Digital Library

[21]

R. M. Nallapati, A. Ahmed, E. P. Xing, and W. W. Cohen, "Joint latent topic models for text and citations," in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008, pp. 542--550.

Digital Library

[22]

A. Ritchie, S. Robertson, and S. Teufel, "Comparing citation contexts for information retrieval," in Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 2008, pp. 213--222.

Digital Library

[23]

E. V. Bernstam, J. R. Herskovic, Y. Aphinyanaphongs, C. F. Aliferis, M. G. Sriram, and W. R. Hersh, "Using citation data to improve retrieval from medline," Journal of the American Medical Informatics Association, vol. 13, no. 1, pp. 96--105, 2006.

[24]

S. Gerrish and D. M. Blei, "A language-based approach to measuring scholarly impact," in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010, pp. 375--382.

[25]

Y. Sun, R. Barber, M. Gupta, C. Aggarwal, and J. Han, "Co-author relationship prediction in heterogeneous bibliographic networks." in Proc. 2011 Int. Conf. Advances in Social Network Analysis and Mining (ASONAM'11), Kaohsiung, Taiwan, 2011, pp. 121--128.

Digital Library

[26]

Y. Sun, J. Han, C. C. Aggarwal, and N. Chawla, "When will it happen? relationship prediction in heterogeneous information networks," in Proc. 2012 ACM Int. Conf. on Web Search and Data Mining (WSDM'12), Seattle, WA, 2012.

Digital Library

[27]

Y. Sun, B. Norick, J. Han, X. Yan, P. S. Yu, and X. Yu, "Integrating meta-path selection with user guided object clustering in heterogeneous information networks," in Proc. of 2012 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'12), Beijing, China, 2012, pp. 1348--1356.

Digital Library

[28]

X. Yu, X. Ren, Y. Sun, B. Sturt, U. Khandelwal, Q. Gu, B. Norick, and J. Han, "Heterec: Entity recommendation in heterogeneous information networks with implicit user feedback," in Proc. of 2013 ACM Int. Conf. Series on Recommendation Systems (RecSys'13), Hong Kong, 2013, pp. 347--350.

Digital Library

[29]

X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han, "Personalized entity recommendation: A heterogeneous information network approach," in Proc. 2014 ACM Int. Conf. on Web Search and Data Mining (WSDM'14), New York, 2014, pp. 283--292.

Digital Library

[30]

Y. Lv, C. Zhai, and W. Chen, "A boosting approach to improving pseudo-relevance feedback," in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, 2011, pp. 165--174.

Digital Library

Cited By

Sun YYin HRen XBarrett RCummings RAgichtein EGabrilovich E(2017)Recommendation in Context-Rich EnvironmentProceedings of the 26th International Conference on World Wide Web Companion10.1145/3041021.3051105(941-945)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3041021.3051105
Huang ZZheng YCheng RSun YMamoulis NLi XKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Meta StructureProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939815(1595-1604)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939815
Liu XYu XGao ZXia TBollen JBlustein JHerder ERubart JAshman H(2016)Comparing Community-based Information Adoption and Diffusion Across Different Microblogging SitesProceedings of the 27th ACM Conference on Hypertext and Social Media10.1145/2914586.2914665(103-112)Online publication date: 10-Jul-2016
https://dl.acm.org/doi/10.1145/2914586.2914665
Show More Cited By

Index Terms

Full-text based context-rich heterogeneous network mining approach for citation recommendation
1. Information systems
  1. Information retrieval

Recommendations

Pre-trained transformer-based citation context-aware citation network embeddings
JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

Academic papers form citation networks wherein each paper is a node and citation relationships between papers are edges. The embeddings of each paper obtained by projecting the citation network into a vector space are called citation network embeddings. ...
Neural Citation Network for Context-Aware Citation Recommendation
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

The accelerating rate of scientific publications makes it difficult to find relevant citations or related work. Context-aware citation recommendation aims to solve this problem by providing a curated list of high-quality candidates given a short passage ...
Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

The sheer volume of scholarly publications available online significantly challenges how scholars retrieve the new information available and locate the candidate reference papers. While classical text retrieval and pseudo relevance feedback (PRF) ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

JCDL '14: Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries

September 2014

498 pages

ISBN:9781479955695

General Chair:
George Buchanan,
Program Chairs:
Martin Klein,
Andreas Rauber,
Sally Jo Cunningham

Sponsors

Publisher

IEEE Press

Publication History

Published: 08 September 2014

Check for updates

Author Tags

Qualifiers

Research-article

Conference

JCDL '14

Sponsor:

JCDL '14: 14th ACM/IEEE-CS Joint Conference on Digital Libraries

September 8 - 12, 2014

London, United Kingdom

Acceptance Rates

Overall Acceptance Rate 415 of 1,482 submissions, 28%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
219
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sun YYin HRen XBarrett RCummings RAgichtein EGabrilovich E(2017)Recommendation in Context-Rich EnvironmentProceedings of the 26th International Conference on World Wide Web Companion10.1145/3041021.3051105(941-945)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3041021.3051105
Huang ZZheng YCheng RSun YMamoulis NLi XKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Meta StructureProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939815(1595-1604)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939815
Liu XYu XGao ZXia TBollen JBlustein JHerder ERubart JAshman H(2016)Comparing Community-based Information Adoption and Diffusion Across Different Microblogging SitesProceedings of the 27th ACM Conference on Hypertext and Social Media10.1145/2914586.2914665(103-112)Online publication date: 10-Jul-2016
https://dl.acm.org/doi/10.1145/2914586.2914665
Duma DSutton CKlein EAdam NCassel BYesha YFuruta RWeigle M(2016)Context MattersProceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries10.1145/2910896.2925431(201-202)Online publication date: 19-Jun-2016
https://dl.acm.org/doi/10.1145/2910896.2925431
Jiang ZLiu XChen Y(2016)Recovering uncaptured citations in a scholarly networkJournal of the Association for Information Science and Technology10.1002/asi.2347567:7(1722-1735)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1002/asi.23475
Jensen SYu YLiu HLiu XGiven L(2015)Query-centric scientific topic evolution extractionProceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community10.5555/2857070.2857197(1-4)Online publication date: 6-Nov-2015
https://dl.acm.org/doi/10.5555/2857070.2857197
Guo CLiu XGiven L(2015)Feature selection on heterogeneous graphProceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community10.5555/2857070.2857189(1-4)Online publication date: 6-Nov-2015
https://dl.acm.org/doi/10.5555/2857070.2857189
Jiang ZLiu XGao LBailey JMoffat AAggarwal Cde Rijke MKumar RMurdock VSellis TYu J(2015)Chronological Citation Recommendation with Information-Need ShiftingProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806567(1291-1300)Online publication date: 17-Oct-2015
https://dl.acm.org/doi/10.1145/2806416.2806567
Liu XYu YGuo CSun YLi JWang XGarofalakis MSoboroff ISuel TWang M(2014)Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation RecommendationProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2661965(121-130)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2661965

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents