Libraries at University of Nebraska-Lincoln


Date of this Version

Fall 9-29-2019



Amoualian, H., Clausel, M., Eric, G., E. , & Amini, M. R. (2016). Streaming-LDA: A Copula-based Approach to Modeling Topic Dependencies in Document Streams. Paper presented at the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining San Francisco, CA, USA. DOI:

Baeza-Yates, R., & Ribeiro-Netoz. (1999). Modern Information Retrieval, : ACM Press. .

Börner , K., Chen, C., & Boyack, K. (2005). Visualizing knowledge domains. . Annual Review of Information Science and Technology, 37(1), 179-255.

Chen, B., Tsutsui, S., Ding, Y., & Ma, F. (2017). Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval. Journal of Informetrics, 11(4), 1175-1189. doi:

Chen, X. Chen, J. Wu, D., Xie, Y. & Li, J., )2016). Mapping the research trends by co-word analysis based on keywords from funded project. Procedia Computer Science 91 (2016) 547 – 555.

Culpepper, J. S., Fernando Diaz, F., & Smucker, M. D. (2018). Research Frontiers in Information Retrieval. Report from the Third Strategic Workshop on Information Retrieval in Lorne

ACM SIGIR Forum, 52(1), 37-90. doi:

D. Hong, D., Park, Y., Lee, J., Shin, V., & Woo, W. (2005). Personalized Information Retrieval Framework. Paper presented at the the First Internaltional Workshop on Personalized Context Modeling and Management for UbiComp Applications.

Ding , Y., Chowdhury, G., G. , & Foo, S. (2000 ). Journal as Markers of Intellectual Space: Journal Co-Citation Analysis of Information Retrieval Area, 1987–1997. Scientometrics,, 47(1), 155-173.;2-B.

Dumais, S., Cutrell, E., Cadiz, J., Jancke, G., Sarin, R., & Robbins, D. (2003). Stuff I’ve seen: a system for personal information retrieval and re-use’, in Paper presented at the 26th annual international ACM SIGIR conference on Research and development in information retrieval, Toronto, Canada. .

Gasparetti, F. (2017). Modeling user interests from web browsing activities. Data mining and knowledge discovery, 31(2), 502-547. doi: 10.1007/s10618-016-0482-x.

Hu, C.P., Hu, J. M. Deng, S.L. & Liu,Y. A. (2013) co-word analysis of library and information science in China. Sciemtometrics, 97(2): 369–382.

Hu, J. Zhang, Y. (2015).Research patterns and trends of Recommendation System in China using co-word analysis, Information Processing & Management, 51: 329-339.

Khan, M., & Mahmood, A. (2018). A distinctive approach to obtain higher page rank through search engine optimization. Sādhanā, 43(3): 43.

Kazemi, N., Modak, N. M. & Govindan, K. (2018). A review of reverse logistics and closed loop supply chain management studies published in IJPR: a bibliometric and content analysis. International Journal of Production Research. DOI: 1080/00207543.2018.1471244

Khasseh, A.K. Soheili, F. Sharif Moghaddam, H. and Mousavi Chelak, A.( 2017). Intellectual structure of knowledge in iMetrics: A co-word analysis. Information Processing & Management. 53 (3) 705-720. DOI:10.1016/j.ipm.2017.02.001

Lee, B. & Jeong, Y.I. (2008). Mapping Korea’s national R&D domain of robot technology by using the co-word analysis, Scientometrics, 77 (2008) 3-19. DOI: 10.1007/s11192-007-1819-4.

Liu, G.Y. Hu, J.M. Wang, H.L. (2012). Co-word analysis of digital library field in China, scientometrics, 91 (2012) 203-217.

Lu, Y., & Hsiao, I.-H. (2017). Personalized Information Seeking Assistant (PiSA): from programming information seeking to learning. Information Retrieval Journal, 20(5), 433-455.

Lucrédio, D., Fortes, R. P. d. M., & Whittle, J. (2012). MOOGLE: a metamodel-based model search engine. Software & Systems Modeling, 11(2), 183-208.

Munan, Li (2018). Visualizing the knowledge profile on self-powered technology. Nano Energy 51, 250-259.

Musgrove, P.B., Binns, R., Page-Kennedy, T., Thelwall, M. A method for identifying clusters in sets of inter-linking Web spaces, Scientometrics, 58 (2003) 657-672.

Rorissa, R., & Yuan, X. (2012). Visualizing and mapping the intellectual structure of information retrieval. Information Processing & Management, 48(1), 120-135. doi:

Shrivastava, P., Ivanaj, S. and Ivanaj, V. (2016) ‘Strategic technological innovation for sustainable development’, Int. J. Technology Management, Vol. 70, No. 1, pp.76–107.

Vijaya, P., Raju, G., & Ray, S. K. (2016). Artificial neural network-based merging score for Meta search engine. Journal of Central South University, 23(10), 2604-2615.

Wang, B.-k., Huang, Y.-f., Yang, W.-x., & Li, X. (2012). Short text classification based on strong feature thesaurus. Journal of Zhejiang University SCIENCE C, 13(9), 649-659.

Wu, D.S., Xie, Y.J. Dai, Q.Z. & Li, J.P. (2016). A systematic overview of operations research/ management science research in Mainland China: Bibliometric analysis of the period 2001-2013, Asia-Pacific Journal of Operational Research, forthcoming.

Zong, Q.J., Shen, H.Z., Yuan, Q.J. , Hu, X.W., Hou, Z.P., Deng, S.G(2013). Doctoral dissertations of Library and Information Science in China: A co-word analysis, Scientometrics, 94 : 781-799.




Today information and communication cause the daily growth of published information. Studying all scientific production content and structures for specialist in different fields and publications is impossible. This study aims to analyze the articles regarding information retrieval based on the concepts of co-occurrence network analysis and centrality indicators published in Clarivate Analytics Web of Science[1] during 1983-2017. This is a descriptive study, using Scientometric approach. Its statistical population contains all articles related to Information retrieval in Clarivate Analytics Web of Science during1983-2017. The scientific research on Information retrieval starts in 2002. Based on the scientific map of countries, America, England, Canada and Singapore have the most articles in information retrieval field. Iran and Brazil have also been active in research on this field from 2012. The top authors of Articles in IR field articles during 1989-2017 are: Spink, Boregman, Chowdhury and Meado. In the analysis of IR field articles based on co-word anlaysis, 8 subject cluster were observed. Among them related to internal and external factors in information retrieval.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.