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Probabilistic Topic Modelling for Controlled Snowball Sampling in Citation Network Collection

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Knowledge Engineering and Semantic Web (KESW 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 786))

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Abstract

The paper presents a probabilistic topic model (PTM) application to citation network collection. Snowball sampling method is moderated with the selection of the most relevant papers by means of the PTM. The PTM used in the paper is modified to treat collections of short texts. It is constructed from the titles of seed papers collection united with the papers obtained through unrestricted snowball sampling. The objective of the research is to propose and to experimentally verify the approach of application of PTM of short text documents for improvement of a citation network collection. The preliminary analysis has shown that the method is robust: seed paper collection variations do not affect the most influencing papers subset in the collected citation network.

H. Dobrovolskyi and N. Keberle—The work has been partially done in frame of the EU FP7 Marie Curie IRSES SemData project (http://www.semdata-project.eu/), grant agreement No PIRSESGA-2013-612551.

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Notes

  1. 1.

    AAAI Digital Library Conference Proceedings. https://aaai.org/Library/conferences-library.php.

  2. 2.

    Journals: Free Texts: Download & Streaming: Internet Archive. https://archive.org/details/journals.

  3. 3.

    Stanford Large Network Dataset Collection. https://snap.stanford.edu/data/.

  4. 4.

    Google Scholar https://scholar.google.com.ua/.

  5. 5.

    Microsoft Academic https://academic.microsoft.com/.

  6. 6.

    Semantic Scholar https://www.semanticscholar.org/.

  7. 7.

    See NLTK Stemmers http://www.nltk.org/howto/stem.html.

  8. 8.

    See NLTK part-of-speech tagger http://www.nltk.org/book/ch05.html.

  9. 9.

    For instance list of English stop words is available at Snowball stemmer site http://snowball.tartarus.org/algorithms/english/stop.txt.

  10. 10.

    https://azure.microsoft.com/en-us/services/cognitive-services/academic-knowledge/.

References

  1. Ahad, A., Fayaz, M., Shah, A.S.: Navigation through citation network based on content similarity using cosine similarity algorithm. Int. J. Database Theory Appl. 9(5), 9–20 (2016)

    Article  Google Scholar 

  2. Aletras, N., Stevenson, M.: Evaluating topic coherence using distributional semantics. IWCS 13, 13–22 (2013)

    Google Scholar 

  3. Barabási, A.L.: Scale-free networks: a decade and beyond. Science 325(5939), 412–413 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  5. Ermolayev, V., Batsakis, S., Keberle, N., Tatarintseva, O., Antoniou, G.: Ontologies of time: Review and trends. Int. J. Comput. Sci. Appl. 11(3), 57–115 (2014)

    Google Scholar 

  6. Fouz-González, J.: Trends and directions in computer-assisted pronunciation training. In: Mompean, J.A., Fouz-González, J. (eds.) Investigating English Pronunciation, pp. 314–342. Palgrave Macmillan UK, London (2015). doi:10.1057/9781137509437_14

    Chapter  Google Scholar 

  7. Garfield, E.: From computational linguistics to algorithmic historiography. In: Symposium in Honor of Casimir Borkowski at the University of Pittsburgh School of Information Sciences (2001)

    Google Scholar 

  8. Garfield, E., Merton, R.K.: Citation Indexing: Its Theory and Application in Science, Technology, and Humanities, vol. 8. Wiley, New York (1979)

    Google Scholar 

  9. Gillis, N.: Introduction to nonnegative matrix factorization. arXiv preprint arXiv:1703.00663 (2017)

  10. Harris, J.K., Beatty, K.E., Lecy, J.D., Cyr, J.M., Shapiro, R.M.: Mapping the multidisciplinary field of public health services and systems research. Am. J. Prev. Med. 41(1), 105–111 (2011)

    Article  Google Scholar 

  11. Hoyer, P.O.: Non-negative sparse coding. In: Proceedings of the 2002 12th IEEE Workshop on Neural Networks for Signal Processing, pp. 557–565. IEEE (2002)

    Google Scholar 

  12. Islam, A., Inkpen, D.: Semantic text similarity using corpus-based word similarity and string similarity. ACM Trans. Knowl. Discov. Data (TKDD) 2(2), 10 (2008)

    Google Scholar 

  13. Jijkoun, V., de Rijke, M.: Recognizing textual entailment: is word similarity enough? In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS, vol. 3944, pp. 449–460. Springer, Heidelberg (2006). doi:10.1007/11736790_25

    Chapter  Google Scholar 

  14. Jolliffe, I.T.: Principal component analysis and factor analysis. Principal component analysis. Springer Series in Statistics, pp. 115–128. Springer, New York (1986). doi:10.1007/978-1-4757-1904-8_7

    Chapter  Google Scholar 

  15. Kajikawa, Y., Ohno, J., Takeda, Y., Matsushima, K., Komiyama, H.: Creating an academic landscape of sustainability science: an analysis of the citation network. Sustain. Sci. 2(2), 221 (2007)

    Article  Google Scholar 

  16. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 1188–1196 (2014)

    Google Scholar 

  17. Lecy, J.D., Beatty, K.E.: Representative literature reviews using constrained snowball sampling and citation network analysis (2012)

    Google Scholar 

  18. Lee, A., et al.: Language-independent methods for computer-assisted pronunciation training. Ph.D. thesis, Massachusetts Institute of Technology (2016)

    Google Scholar 

  19. Lin, C.J.: Projected gradient methods for nonnegative matrix factorization. Neural Comput. 19(10), 2756–2779 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  20. Liu, J.S., Lu, L.Y., Lu, W.M., Lin, B.J.: Data envelopment analysis 1978–2010: a citation-based literature survey. Omega 41(1), 3–15 (2013)

    Article  Google Scholar 

  21. López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013)

    Article  Google Scholar 

  22. Lu, Z., Li, H.: A deep architecture for matching short texts. In: Advances in Neural Information Processing Systems, pp. 1367–1375 (2013)

    Google Scholar 

  23. MacKay, D.J.: Information Theory. Inference and Learning Algorithms. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  24. Meho, L.I.: The rise and rise of citation analysis. Phys. World 20(1), 32 (2007)

    Article  Google Scholar 

  25. Mihalcea, R., Corley, C., Strapparava, C., et al.: Corpus-based and knowledge-based measures of text semantic similarity. AAAI 6, 775–780 (2006)

    Google Scholar 

  26. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  27. Moya-Anegón, F., Vargas-Quesada, B., Herrero-Solana, V., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., Munoz-Fernández, F.: A new technique for building maps of large scientific domains based on the cocitation of classes and categories. Scientometrics 61(1), 129–145 (2004)

    Article  Google Scholar 

  28. Newman, M.E.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. 98(2), 404–409 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  29. Newman, M.E.: Coauthorship networks and patterns of scientific collaboration. Proc. Natl. Acad. Sci. 101(suppl 1), 5200–5205 (2004)

    Article  Google Scholar 

  30. Pang, J., Li, X., Xie, H., Rao, Y.: SBTM: topic modeling over short texts. In: Gao, H., Kim, J., Sakurai, Y. (eds.) DASFAA 2016. LNCS, vol. 9645, pp. 43–56. Springer, Cham (2016). doi:10.1007/978-3-319-32055-7_4

    Chapter  Google Scholar 

  31. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. EMNLP 14, 1532–1543 (2014)

    Google Scholar 

  32. Petticrew, M., Gilbody, S.: Planning and conducting systematic reviews. In: Health Psychology in Practice, pp. 150–179 (2004)

    Google Scholar 

  33. Popova, S., Khodyrev, I., Egorov, A., Logvin, S., Gulyaev, S., Karpova, M., Mouromtsev, D.: Sci-search: academic search and analysis system based on keyphrases. In: Klinov, P., Mouromtsev, D. (eds.) KESW 2013. CCIS, vol. 394, pp. 281–288. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41360-5_24

    Chapter  Google Scholar 

  34. Price, D.: Citation measures of hard science, soft science, technology, and nonscience. In: Nelson, C.E., Pollack, D.K. (eds.) Communication Among Scientists and Engineers. Heath Lexington Books Massachusetts (1970)

    Google Scholar 

  35. Ramage, D., Rafferty, A.N., Manning, C.D.: Random walks for text semantic similarity. In: Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing, pp. 23–31. Association for Computational Linguistics (2009)

    Google Scholar 

  36. Small, H.: Visualizing science by citation mapping. J. Associat. Inf. Sci. Technol. 50(9), 799 (1999)

    Google Scholar 

  37. Socher, R., Huang, E.H., Pennin, J., Manning, C.D., Ng, A.Y.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in Neural Information Processing Systems, pp. 801–809 (2011)

    Google Scholar 

  38. de Solla Price, D.J.: Networks of scientific papers. Science 149(3683), 510–515 (1965)

    Article  Google Scholar 

  39. Vorontsov, K., Potapenko, A.: Tutorial on probabilistic topic modeling: additive regularization for stochastic matrix factorization. In: Ignatov, D.I., Khachay, M.Y., Panchenko, A., Konstantinova, N., Yavorskiy, R.E. (eds.) AIST 2014. CCIS, vol. 436, pp. 29–46. Springer, Cham (2014). doi:10.1007/978-3-319-12580-0_3

    Google Scholar 

  40. Yan, X., Guo, J., Lan, Y., Cheng, X.: A biterm topic model for short texts. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1445–1456. ACM (2013)

    Google Scholar 

  41. Yan, X., Guo, J., Liu, S., Cheng, X., Wang, Y.: Learning topics in short texts by non-negative matrix factorization on term correlation matrix. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 749–757. SIAM (2013)

    Google Scholar 

  42. Yang, K., Meho, L.I.: Citation analysis: a comparison of Google Scholar, Scopus, and web of science. Proc. Am. Soc. Inf. Sci. Technol. 43(1), 1–15 (2006). http://dx.doi.org/10.1002/meet.14504301185

    Google Scholar 

  43. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A.J., Hovy, E.H.: Hierarchical attention networks for document classification. In: HLT-NAACL, pp. 1480–1489 (2016)

    Google Scholar 

  44. Zuo, Y., Zhao, J., Xu, K.: Word network topic model: a simple but general solution for short and imbalanced texts. Knowl. Inf. Syst. 48(2), 379–398 (2016)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Zaporizhzhya National University, Zhukovskogo St. 66, Zaporizhzhya, 69600, Ukraine

    Hennadii Dobrovolskyi, Nataliya Keberle & Olga Todoriko

Authors
  1. Hennadii Dobrovolskyi
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  2. Nataliya Keberle
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  3. Olga Todoriko
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Correspondence to Hennadii Dobrovolskyi .

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Editors and Affiliations

  1. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Przemysław Różewski

  2. University of Bonn, Bonn, Germany

    Christoph Lange

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Dobrovolskyi, H., Keberle, N., Todoriko, O. (2017). Probabilistic Topic Modelling for Controlled Snowball Sampling in Citation Network Collection. In: Różewski, P., Lange, C. (eds) Knowledge Engineering and Semantic Web. KESW 2017. Communications in Computer and Information Science, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-69548-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-69548-8_7

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