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The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Investigating Software Usage in the Social Sciences: A Knowledge Graph Approach

Schindler, David, Zapilko, Benjamin, Krüger, Frank·2020·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-49461-2_16PMC PMC7250610
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Crossref · 10.1007/978-3-030-49461-2_16

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  • Software Ontology (SO) is used in [60] to extract software mentions by employing a neural network-based classifier in scientific documents.

    “In the literature, Software Ontology (SO) is used in [60] that extracts software mentions by employing neural network-based classifier in the scientific documents.”

    From Scholarly knowledge graphs through structuring scholarly communication: a review (2023) · cites this paper as [60]

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Citations

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Research works printed but not linked: 28

  1. Boland, K., Krüger, F.: Distant supervision for silver label generation of software mentions in social scientific publications. In: Proceedings of the BIRNDL 2019, Paris, France, July 2019. http://ceur-ws.org/Vol-2414/paper3.pdf
  2. Duck, G., Nenadic, G., Brass, A., Robertson, D.L., Stevens, R.: bioNerDS: exploring bioinformatics’ database and software use through literature mining. BMC Bioinformatics 14(1), 194 (2013) · DOI
  3. Duck, G., Nenadic, G., Filannino, M., Brass, A., Robertson, D.L., Stevens, R.: A survey of bioinformatics database and software usage through mining the literature. PLoS ONE 11(6), e0157989 (2016) · DOI
  4. Eklund, A., Nichols, T.E., Knutsson, H.: Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. In: Proceedings of the National Academy of Sciences, p. 201602413 (2016) · DOI · Paper page
  5. Giorgi, J.M., Bader, G.D.: Transfer learning for biomedical named entity recognition with neural networks. Bioinformatics 34(23), 4087–4094 (2018). https://doi.org/10.1093/bioinformatics/bty449 · DOI
  6. Greuel, G.-M., Sperber, W.: swMATH – an information service for mathematical software. In: Hong, H., Yap, C. (eds.) ICMS 2014. LNCS, vol. 8592, pp. 691–701. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44199-2_103 · DOI
  7. Heath, T., Bizer, C.: Linked data: evolving the web into a global data space. Synth. Lect. Semant. Web Theory Technol. 1(1), 1–136 (2011) · DOI
  8. Howison, J., Bullard, J.: Software in the scientific literature: problems with seeing, finding, and using software mentioned in the biology literature. J. Assoc. Inf. Sci. Technol. 67(9), 2137–2155 (2016) · DOI
  9. Jaradeh, M.Y., et al.: Open research knowledge graph: next generation infrastructure for semantic scholarly knowledge. In: Proceedings of the K-Cap, pp. 243–246. ACM (2019) · DOI
  10. Krüger, F., Schindler, D.: A literature review on methods for the extraction of usage statements of software and data. IEEE Comput. Sci. Eng. (2019). https://doi.org/10.1109/MCSE.2019.2943847 · DOI
  11. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco (2001)
  12. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016) · DOI · Paper page
  13. Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977) · DOI · Paper page
  14. Luan, Y., He, L., Ostendorf, M., Hajishirzi, H.: Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. In: Proceedings of the EMNLP (2018) · DOI
  15. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
  16. Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2, pp. 1003–1011. Association for Computational Linguistics (2009) · DOI
  17. Nuzzolese, A.G., Gentile, A.L., Presutti, V., Gangemi, A.: Conference linked data: the scholarlydata project. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 150–158. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_16 · DOI
  18. Pan, X., Yan, E., Wang, Q., Hua, W.: Assessing the impact of software on science: a bootstrapped learning of software entities in full-text papers. J. Informetr. 9(4), 860–871 (2015) · DOI
  19. Pyysalo, S., Ginter, F., Moen, H., Salakoski, T., Ananiadou, S.: Distributional semantics resources for biomedical text processing. In: Proceedings of LBM 2013 (2013)
  20. Ratner, A.J., Bach, S.H., Ehrenberg, H.R., Ré, C.: Snorkel: fast training set generation for information extraction. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1683–1686. ACM (2017) · DOI
  21. Rebholz-Schumann, D., et al.: CALBC silver standard corpus. J. Bioinform. Comput. Biol. 08(01), 163–179 (2010). https://doi.org/10.1142/s0219720010004562 · DOI
  22. Buscaldi, D., Dessì, D., Motta, E., Osborne, F., Reforgiato Recupero, D.: Mining scholarly publications for scientific knowledge graph construction. In: Hitzler, P., et al. (eds.) ESWC 2019. LNCS, vol. 11762, pp. 8–12. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32327-1_2 · DOI
  23. Ruder, S.: Neural transfer learning for natural language processing. Ph.D. thesis, National University of Ireland, Galway (2019)
  24. Russo, D., Voigt, C.C.: The use of automated identification of bat echolocation calls in acoustic monitoring: a cautionary note for a sound analysis. Ecol. Ind. 66, 598–602 (2016). https://doi.org/10.1016/j.ecolind.2016.02.036 · DOI
  25. Smith, A.M., Katz, D.S., Niemeyer, K.E.: Software citation principles. PeerJ Comput. Sci. 2, e86 (2016). https://doi.org/10.7717/peerj-cs.86 · DOI
  26. Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S., Tsujii, J.: BRAT: a web-based tool for NLP-assisted text annotation. In: Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the ACL, pp. 102–107. ACL (2012)
  27. Weichselbraun, A., Kuntschik, P., Brasoveanu, A.M.: Name variants for improving entity discovery and linking. In: Proceedings of the LDK 2019. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2019)
  28. Zeeberg, B.R., et al.: Mistaken identifiers: gene name errors can be introduced inadvertently when using excel in bioinformatics. BMC Bioinformatics 5(1), 80 (2004). https://doi.org/10.1186/1471-2105-5-80 · DOI · Paper page

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Metadata

Authors
Schindler, D., Zapilko, B., & Krüger, F.
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2020
Pages
271-286
ISSN
  • 0302-9743
  • 1611-3349
DOI
10.1007/978-3-030-49461-2_16
PMC
PMC7250610
Cited by
24
References
2
Language
en
Licence

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