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Researcher Name Disambiguation: Feature Learning and Affinity Propagation Clustering

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Foundations of Intelligent Systems (ISMIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11177))

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Abstract

Name ambiguity has been considered as a challenging task in the field of information retrieval. When we want to query all the papers of a researcher in the current literature integration system, we will find that many irrelevant papers written by the same researcher name appear in the retrieval results, which seriously affect the quality of retrieval. To tackle this problem, name disambiguation task was proposed to correctly distinguish the papers, thus making papers contained in each part belongs to a unique researcher. Certain information sources can help disambiguate researchers, e.g., CoResearcher, affiliation, homepages and paper titles. However, such information sources may be costly to obtain or unavailable. Therefore, it is necessary to solve name disambiguation task under the condition of insufficient information sources. Another challenge is how to accomplish the task without knowing the number of distinct researchers. In this paper, we sufficiently use the relational network between papers. Our proposed method learns the feature representations of papers and then uses affinity propagation clustering to solve name disambiguation task. The experimental results show that our proposed method can obtain better accuracy at solving name disambiguation task comparing to existing methods.

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References

  1. Hasan, M.A., Baichuan, Z.: Name disambiguation in anonymized graphs using network embedding. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1239–1248. ACM, Singapore (2017)

    Google Scholar 

  2. Shadbolt, N.R., Mcrae-Spencer, D.M.: Also by the same author: Aktiveauthor, a citation graph approach to name disambiguation. In: ACM/IEEE Joint Conference on Digital Libraries, pp. 53–54. ACM (2006)

    Google Scholar 

  3. Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and DataMining, pp. 855–864. ACM (2016)

    Google Scholar 

  4. Wang, X., Tang, J., Cheng, H., Yu, P.S.: ADANA: active name disambiguation. In: 11th IEEE International Conference on Data Mining, pp. 794–803. IEEE, Vancouver (2011)

    Google Scholar 

  5. Tran, H.N., Huynh, T., Do, T.: Author name disambiguation by using deep neural network. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014. LNCS (LNAI), vol. 8397, pp. 123–132. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05476-6_13

    Chapter  Google Scholar 

  6. Hermansson, L., Kerola, T., Johansson, F.: Entity disambiguation in anonymized graphs using graph kernels. In: 22nd ACM International Conference on Information and Knowledge Management, pp. 1037–1046. ACM, San Francisco (2013)

    Google Scholar 

  7. Zhu, J., Wu, X., Lin, X.: A novel multiple layers name disambiguation framework for digital libraries using dynamic clustering. Scientometrics 114(3), 781–794 (2018)

    Article  Google Scholar 

  8. Zhang, B., Choudhury, S., Hasan, M.A., Ning, X., Agarwal, K., Purohit, S.: Trust from the past: Bayesian personalized ranking based link prediction in knowledge graphs. CoRR (2016)

    Google Scholar 

  9. Zhang, B., Saha, T.K., Hasan, M.A.: Name disambiguation from link data in a collaboration graph. Soc. Netw. Anal. Min. 5, 11 (2014)

    Google Scholar 

  10. On, B., Lee, I., Lee, D.: Scalable clustering methods for the name disambiguation problem. Knowl. Inf. Syst. 31(1), 129–151 (2012)

    Article  Google Scholar 

  11. Mann, G.S., Yarowsky, D.: Unsupervised personal name disambiguation. In: Proceedings of the Seventh Conference on Natural Language Learning, pp. 33–40. ACL, Edmonton (2003)

    Google Scholar 

  12. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM, New York (2014)

    Google Scholar 

  13. Chen, C., Hu, J., Wang, H.: Clustering technique in multi-document personal name disambiguation. In: Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 88–95, Singapore (2009)

    Google Scholar 

  14. Saha, T.K., Zhang, B., Hasan, M.A.: Name disambiguation from link data in a collaboration graph using temporal and topological features. Soc. Netw. Anal. Min. 5(1), 1–14 (2015)

    Article  Google Scholar 

  15. Han, H., Giles, L., Zha, H., Li, C., Tsioutsiouliklis, K.: Two supervised learning approaches for name disambiguation in author citations. In: ACM/IEEE Joint Conference on Digital Libraries, pp. 296–305. ACM, Tucson (2004)

    Google Scholar 

  16. Cen, L., Dragut, E.C., Si, L., Ouzzani, M.: Author disambiguation by hierarchical agglomerative clustering with adaptive stopping criterion. In: The 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 741–744. ACM, Dublin (2013)

    Google Scholar 

  17. Tang, J., Fong, A.C.M., Wang, B., Zhang, J.: A unified probabilistic framework for name disambiguation in digital library. IEEE Trans. Knowl. Data Eng. 24(6), 975–987 (2012)

    Article  Google Scholar 

  18. Zhang, D., Tang, J., Li, J., Wang, K.: A constraint based probabilistic framework for name disambiguation. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 1019–1022. ACM, Lisbon (2007)

    Google Scholar 

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China under grant 61572226, and Jilin Province Key Scientific and Technological Research and Development project under grants 20180201067GX and 20180201044GX.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Zhizhi Yu & Bo Yang

  2. Key Laboratory of Symbol Computation and Knowledge Engineering, (Jilin University), Ministry of Education, Changchun, 130012, China

    Zhizhi Yu & Bo Yang

Authors
  1. Zhizhi Yu
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  2. Bo Yang
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Corresponding author

Correspondence to Bo Yang .

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Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. American University, Washington, DC, USA

    Nathalie Japkowicz

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Jiming Liu

  4. University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Yu, Z., Yang, B. (2018). Researcher Name Disambiguation: Feature Learning and Affinity Propagation Clustering. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_22

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  • DOI: https://doi.org/10.1007/978-3-030-01851-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01850-4

  • Online ISBN: 978-3-030-01851-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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