Abstract
In the scientific digital libraries, some papers from different research communities can be described by community-dependent keywords even if they share a semantically similar topic. Articles that are not tagged with enough keyword variations are poorly indexed in any information retrieval system which limits potentially fruitful exchanges between scientific disciplines. In this paper, we introduce a novel experimentally designed pipeline for multi-label semantic-based tagging developed for open-access metadata digital libraries. The approach starts by learning from a standard scientific categorization and a sample of topic tagged articles to find semantically relevant articles and enrich its metadata accordingly. Our proposed pipeline aims to enable researchers reaching articles from various disciplines that tend to use different terminologies. It allows retrieving semantically relevant articles given a limited known variation of search terms. In addition to achieving an accuracy that is higher than an expanded query based method using a topic synonym set extracted from a semantic network, our experiments also show a higher computational scalability versus other comparable techniques. We created a new benchmark extracted from the open-access metadata of a scientific digital library and published it along with the experiment code to allow further research in the topic.
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.
Excellence Initiative of Scientific and Technical Information https://www.istex.fr/.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
References
Abrizah, A., Zainab, A.N., Kiran, K., Raj, R.G.: LIS journals scientific impact and subject categorization: a comparison between web of science and scopus. Scientometrics 94(2), 721–740 (2013). https://doi.org/10.1007/s11192-012-0813-7
Blei, D.M., McAuliffe, J.D.: Supervised topic models. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 3–6 December 2007, pp. 121–128. Curran Associates, Inc. (2007)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Bodrunova, S., Koltsov, S., Koltsova, O., Nikolenko, S., Shimorina, A.: Interval semi-supervised LDA: classifying needles in a haystack. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013. LNCS (LNAI), vol. 8265, pp. 265–274. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45114-0_21
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. TACL 5, 135–146 (2017)
Borgida, A., Sowa, J.F.: Principles of semantic networks - Explorations in the representation of knowledge. The Morgan Kaufmann Series in Representation and Reasoning. Morgan Kaufmann, Burlington (1991)
Halko, N., Martinsson, P.G., Tropp, J.A.: Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev. 53(2), 217–288 (2011)
Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015). https://doi.org/10.3233/SW-140134
Liang, F., Yang, Y., Bradley, J.: Large scale topic modeling: improvements to LDA on Apache Spark, September 2015. https://tinyurl.com/y7xfqnze
Mahdisoltani, F., Biega, J., Suchanek, F.M.: YAGO3: a knowledge base from multilingual wikipedias. In: CIDR 2015, Asilomar, CA, USA, 4–7 January 2015 (2015). www.cidrdb.org
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting held, 5–8 December 2013, Lake Tahoe, Nevada, United States, pp. 3111–3119 (2013)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM (CACM) 38(11), 39–41 (1995). https://doi.org/10.1145/219717.219748
Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)
Pinto, J.M.G., Balke, W.: Demystifying the semantics of relevant objects in scholarly collections: a probabilistic approach. In: Proceedings of the 15th ACM/IEEE-CE Joint Conference on Digital Libraries, Knoxville, TN, USA, 21–25 June 2015, pp. 157–164. ACM (2015). https://doi.org/10.1145/2756406.2756923
Ramage, D., Hall, D.L.W., Nallapati, R., Manning, C.D.: Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, 6–7 August 2009, Singapore, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 248–256. ACL (2009)
Sacco, G.M., Tzitzikas, Y. (eds.): Dynamic Taxonomies and Faceted Search: Theory, Practice, and Experience. The Information Retrieval Series. Springer, Berlin, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02359-0
Scientific and Technical Information Department - CNRS: White Paper - Open Science in a Digital Republic. OpenEdition Press, Marseille (2016). https://doi.org/10.4000/books.oep.1635
Wawrzinek, J., Balke, W.-T.: Semantic facettation in pharmaceutical collections using deep learning for active substance contextualization. In: Choemprayong, S., Crestani, F., Cunningham, S.J. (eds.) ICADL 2017. LNCS, vol. 10647, pp. 41–53. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70232-2_4
Zhou, P., Wei, J., Qin, Y.: A semi-supervised text clustering algorithm with word distribution weights. In: Proceedings of the 2013 the International Conference on Education Technology and Information System (ICETIS 2013). Advances in Intelligent Systems Research, 21–22 June 2013, Sanya, China, pp. 1024–1028. Atlantis Press (2013). https://doi.org/10.2991/icetis-13.2013.235
Acknowledgment
We would like to thank ARC6 program (http://www.arc6-tic.rhonealpes.fr/larc-6/) of the Region Auvergne-Rhône-Alpes that funds the current PhD studies of the first author and thank ISTEX project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Al-Natsheh, H.T., Martinet, L., Muhlenbach, F., Rico, F., Zighed, D.A. (2018). Metadata Enrichment of Multi-disciplinary Digital Library: A Semantic-Based Approach. In: Méndez, E., Crestani, F., Ribeiro, C., David, G., Lopes, J. (eds) Digital Libraries for Open Knowledge. TPDL 2018. Lecture Notes in Computer Science(), vol 11057. Springer, Cham. https://doi.org/10.1007/978-3-030-00066-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-00066-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00065-3
Online ISBN: 978-3-030-00066-0
eBook Packages: Computer ScienceComputer Science (R0)