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Enrichment of Academic Search Engine Results Pages by Citation-Based Graphs

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Information Retrieval Technology (AIRS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9460))

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Abstract

Researchers’ readings of academic papers make their research more sophisticated and objective. In this paper, we describe a method of supporting scholarly surveys by incorporating a graph based on citation relationships into the results page of an academic search engine. Conventional academic search engines have a problem in that users have difficulty in determining which academic papers are relevant to their needs because it is hard to understand the relationship between the academic papers that appear in the search results pages. Our method helps users to make judgments about the relevance of papers by clearly visualizing the relationship. It visualizes not only academic papers on the results page but also papers that have a strong citation relationship with them. We carefully considered the method of visualization and implemented a prototype with which we conducted a user study simulating scholarly surveys. We confirmed that our method improved the efficiency of scholarly surveys through the user study.

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Notes

  1. 1.

    http://scholar.google.com/.

  2. 2.

    http://academic.research.microsoft.com/.

  3. 3.

    http://citeseerx.ist.psu.edu/index.

References

  1. Jacsó, P.: Google Scholar: the pros and the cons. Online Inf. Rev. 29(2), 208–214 (2005)

    Article  Google Scholar 

  2. Beel, J., Gipp, B., Wilde, E.: Academic Search Engine Optimization (ASEO): optimizing scholarly literature for Google Scholar and co. J. Sch. Publ. 41(2), 176–190 (2010)

    Google Scholar 

  3. Beel, J., Gipp, B.: Academic search engine spam and Google Scholar’s resilience against it. J. Electron. Publ. 13(3) (2010)

    Google Scholar 

  4. Verberne, S., Sappelli, M., Kraaij, W.: Query term suggestion in academic search. In: de Rijke, M., Kenter, T., de Vries, A.P., Zhai, C.X., de Jong, F., Radinsky, K., Hofmann, K. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 560–566. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  5. Viégas, F.B., Donath, J.: Social network visualization: can we go beyond the graph? In: Workshop on Social Networks, CSCW, vol. 4, pp. 6–10 (2004)

    Google Scholar 

  6. Nanba, H., Abekawa, T., Okumura, M., Saito, S.: Bilingual PRESRI - integration of multiple research paper databases. In: Proceedings of RIAO, pp. 195–211 (2004)

    Google Scholar 

  7. He, Q., Chen, B., Pei, J., Qiu, B., Mitra, P., Giles, C.L.: Detecting topic evolution in scientific literature: how can citations help? In: Proceedings of CIKM, pp. 957–966 (2009)

    Google Scholar 

  8. Dunne, C., Shneiderman, B., Gove, R., Klavans, J., Dorr, B.J.: Rapid understanding of scientific paper collections: integrating statistics, text analytics, and visualization. JASIST 63(12), 2351–2369 (2012)

    Article  Google Scholar 

  9. Varadarajan, R., Hristidis, V.: A system for query-specific document summarization. In: Proceedings of CIKM, pp. 622–631 (2006)

    Google Scholar 

  10. Clarke, C.L.A., Agichtein, E., Dumais, S.T., White, R.W.: The influence of caption features on clickthrough patterns in web search. In: Proceedings of SIGIR, pp. 135–142 (2007)

    Google Scholar 

  11. Nguyen, T., Zhang, J.: A novel visualization model for web search results. IEEE Trans. Vis. Comput. Graph. 12(5), 981–988 (2006)

    Article  Google Scholar 

  12. Giacomo, E.D., Didimo, W., Grilli, L., Liotta, G.: Graph visualization techniques for web clustering engines. IEEE Trans. Vis. Comput. Graph. 13(2), 294–304 (2007)

    Article  Google Scholar 

  13. Scaiella, U., Ferragina, P., Marino, A., Ciaramita, M.: Topical clustering of search results. In: Proceedings of WSDM, pp. 223–232 (2012)

    Google Scholar 

  14. Mirylenka, D., Passerini, A.: Navigating the topical structure of academic search results via the wikipedia category network. In: Proceedings of CIKM, pp. 891–896 (2013)

    Google Scholar 

  15. Kim, Y., Seo, J., Croft, W.B.: Automatic boolean query suggestion for professional search. In: Proceedings of SIGIR, pp. 825–834 (2011)

    Google Scholar 

  16. Yan, R., Tang, J., Liu, X., Shan, D., Li, X.: Citation count prediction: learning to estimate future citations for literature. In: Proceedings of CIKM, pp. 1247–1252 (2011)

    Google Scholar 

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Acknowledgments

We thank Associate Professor Hidetsugu Nanba at Hiroshima City University for his valuable comments on our research.

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Authors and Affiliations

  1. Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan

    Shuhei Shogen, Toshiyuki Shimizu & Masatoshi Yoshikawa

Authors
  1. Shuhei Shogen
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  2. Toshiyuki Shimizu
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  3. Masatoshi Yoshikawa
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Corresponding author

Correspondence to Toshiyuki Shimizu .

Editor information

Editors and Affiliations

  1. Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia

    Guido Zuccon

  2. Brisbane, Queensland, Australia

    Shlomo Geva

  3. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  4. RMIT University, Melbourne, Australia

    Falk Scholer

  5. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Aixin Sun

  6. Tianjin University, China

    Peng Zhang

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Shogen, S., Shimizu, T., Yoshikawa, M. (2015). Enrichment of Academic Search Engine Results Pages by Citation-Based Graphs. In: Zuccon, G., Geva, S., Joho, H., Scholer, F., Sun, A., Zhang, P. (eds) Information Retrieval Technology. AIRS 2015. Lecture Notes in Computer Science(), vol 9460. Springer, Cham. https://doi.org/10.1007/978-3-319-28940-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-28940-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28939-7

  • Online ISBN: 978-3-319-28940-3

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