Abstract
Throughout the history of science, different knowledge areas have collaborated to overcome major research challenges. The task of associating a researcher with such areas makes a series of tasks feasible such as the organization of digital repositories, expertise recommendation and the formation of research groups for complex problems. In this paper we propose a simple yet effective automatic classification model that is capable of categorizing research expertise according to a hierarchical knowledge area classification scheme. Our proposal relies on discriminative evidence provided by the title of academic works, which is the minimum information capable of relating a researcher to its knowledge area. We also evaluate the use of learning-to-rank as an effective mean to rank experts with minimum information. Our experiments show that using supervised machine learning methods trained with manually labeled information, it is possible to produce effective classification and ranking models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
In this paper we use the terms categorization and classification interchangeably.
- 6.
- 7.
- 8.
References
Aletras, N., Baldwin, T., Lau, J.H., Stevenson, M.: Representing topics labels for exploring digital libraries. In: Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 239–248 (2014)
Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co. Inc., Boston (1999)
Bakalov, A., McCallum, A., Wallach, H., Mimno, D.: Topic models for taxonomies. In: Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 237–240 (2012)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chen, M., Jin, X., Shen, D.: Short text classification improved by learning multi-granularity topics. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, pp. 1776–1781 (2011)
Chen, Y., Fox, E.A.: Using ACM DL paper metadata as an auxiliary source for building educational collections. In: Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 137–140 (2014)
de Sá, C.C., Gonçalves, M.A., Sousa, D.X., Salles, T.: Generalized BROOF-L2R: a general framework for learning to rank based on boosting and random forests. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 95–104 (2016)
Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133–3181 (2014)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York (2001)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)
Lane, J.: Let’s make science metrics more scientific. Nature 464(7288), 488–489 (2010)
Li, M., Liu, L., Li, C.-B.: An approach to expert recommendation based on fuzzy linguistic method and fuzzy text classification in knowledge management systems. Exp. Syst. Appl. 38(7), 8586–8596 (2011)
Macdonald, C., Ounis, I.: Voting techniques for expert search. Knowl. Inf. Syst. 16(3), 259–280 (2008)
Moreira, C., Calado, P., Martins, B.: Learning to rank for expert search in digital libraries of Academic publications. In: Antunes, L., Pinto, H.S. (eds.) Progress in Artificial Intelligence, pp. 431–445. Springer, Heidelberg (2011)
Niu, W., Liu, Z., Caverlee, J.: On local expert discovery via geo-located crowds, queries, and candidates. ACM Trans. Spatial Algorithms Syst. 2(4), 14:1–14:24 (2016)
Qin, T., Liu, T.-Y., Xu, J., Li, H.: Letor: a benchmark collection for research on learning to rank for information retrieval. Inf. Retr. 13(4), 346–374 (2010)
Ribeiro, I.S., Santos, R.L.T., Gonçalves, M.A., Laender, A.H.F.: On tag recommendation for expertise profiling: a case study in the scientific domain. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 189–198 (2015)
Ribeiro-Neto, B.A., Laender, A.H.F., de Lima, L.R.S.: An experimental study in automatically categorizing medical documents. JASIST 52(5), 391–401 (2001)
Sanchez, D., Moreno, A.: Bringing taxonomic structure to large digital libraries. Int’l. J. Metadata Semant. Ontol. 2(2), 112–122 (2007)
Seymour, E., Damle, R., Sette, A., Peters, B.: Cost sensitive hierarchical document classification to triage PubMed abstracts for manual curation. BMC Bioinform. 12(1), 482 (2011)
Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Mining Knowl. Disc. 22(1–2), 31–72 (2011)
Srinivasan, V., Fox, E.: Progress towards automated ETD cataloging. In: Proceedings of the 19th International Symposium on Electronic theses, dissertations: Data and dissertations (2016)
Waltinger, U., Mehler, A., Lösch, M., Horstmann, W.: Hierarchical classification of OAI metadata using the DDC taxonomy. In: Bernardi, R., Anderson, S., Bjrn, C., Frdrique, G., Zaihrayeu, S. (eds.) Advanced Language Technologies for Digital Libraries, pp. 29–40. Springer, Heidelberg (2011)
Yang, K.-W., Huh, S.-Y.: Automatic expert identification using a text categorization technique in knowledge management systems. Expert Syst. Appl. 34(2), 1445–1455 (2008)
Yang, Y.: An evaluation of statistical approaches to text categorization. Inf. Retr. J. 1(1–2), 69–90 (1999)
Yang, Y., Gopal, S.: Multilabel classification with meta-level features in a learning-to-rank framework. Mach. Learn. 88(1), 47–68 (2012)
Acknowledgements
This work was partially funded by projects InWeb (grant MCT/CNPq 573871/2008-6) and MASWeb (grant FAPEMIG/PRONEX APQ-01400-14), and by the authors’ individual grants from CAPES, CNPq and FAPEMIG.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
de Siqueira, G.O., Canuto, S., Gonçalves, M.A., Laender, A.H.F. (2017). Automatic Hierarchical Categorization of Research Expertise Using Minimum Information. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2017. Lecture Notes in Computer Science(), vol 10450. Springer, Cham. https://doi.org/10.1007/978-3-319-67008-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-67008-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67007-2
Online ISBN: 978-3-319-67008-9
eBook Packages: Computer ScienceComputer Science (R0)