Abstract
This paper presents a formalization and extension of a novel approach to support high-quality content in digital libraries. Building on the concept of plausibility used in cognitive sciences, we aim at judging the plausibility of new scientific papers in light of prior knowledge. In particular, our work proposes a novel assessment of scientific papers to qualitatively support the work of reviewers. To do this, our approach focuses on the key aspect of scientific papers: claims. Claims are sentences found in empirical scientific papers that state statistical associations between entities and correspond to the core contributions of the papers. We can find these types of claims, for instance, in medicine, chemistry, and biology, where the consumption of a drug, a substance, or a product causes an effect on some other type of entity such as a disease, or another drug or substance. To operationalize the notion of plausibility, we promote claims as first-class citizens for scientific digital libraries and exploit state-of-the-art neural embedding representations of text and topic models. As a proof of concept of the potential usefulness of this notion of plausibility, we study and report extensive experiments on documents with scientific papers from the PubMed digital library.
Similar content being viewed by others
Notes
PubMed comprises more than 28 million citations for biomedical literature from MEDLINE, life science journals, and online books.
More information about UMLS in https://www.nlm.nih.gov/research/umls/.
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Kaiser, L., Kudlur, M., Levenberg, J., Man, D., Monga, R., Moore, S., Murray, D., Shlens, J., Steiner, B., Sutskever, I., Tucker, P., Vanhoucke, V., Vasudevan, V., Vinyals, O., Warden, P., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv:1603.04467v2 p. 19 (2015). URLhttp://download.tensorflow.org/paper/whitepaper2015.pdf
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: International Conference on Learning Representations, pp. 1–15 (2015). https://doi.org/10.1146/annurev.neuro.26.041002.131047
Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003). https://doi.org/10.1162/153244303322533223
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994). https://doi.org/10.1109/72.279181
Bertsimas, D., Tsitsiklis, J.N.: Introduction to Linear Optimization. Athena Scientific, Belmont (1997)
Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77 (2012)
Blei, D.M., Lafferty, J.D.: Topic models. In: Srivastava AN, Sahami M (eds) Text Mining: Classification, Clustering, and Applications, chap. 4. Data Mining and Knowledge Discovery Series, Chapman & Hall/CRC, pp. 71–89 (2009). https://doi.org/10.1145/1143844.1143859
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003). https://doi.org/10.1162/jmlr.2003.3.4-5.993
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information 5, 135–146 (2016). DOI 1511.09249v1. arXiv:1607.04606
Chollet, F.: Deep Learning with Python, 1st edn. Manning Publications, Shelter Island (2017)
Chollet, F., others: Keras. (2015) https://github.com/keras-team/keras
Ciccarese, P., Wu, E., Wong, G., Ocana, M., Kinoshita, J., Ruttenberg, A., Clark, T.: The SWAN biomedical discourse ontology. J. Biomed. Inform. 41(5), 739–751 (2008). https://doi.org/10.1016/j.jbi.2008.04.010
Connell, L., Keane, M.T.: A model of plausibility. Cognit. Sci. 30(1), 95–120 (2006). https://doi.org/10.1207/s15516709cog0000_53
Dalvi, N., Ré, C., Suciu, D.: Probabilistic databases: diamonds in the dirt. Commun. ACM 52(7), 86–94 (2009). https://doi.org/10.1145/1538788.1538810
González Pinto J.M.; Balke, W.T.: Can plausibility help to support high quality content in digital libraries? In: TPDL 2017 21st International Conference on Theory and Practice of Digital Libraries. Thessaloniki, Greece (2017)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, vol. 521(7553). MIT Press, Cambridge (2016). https://doi.org/10.1038/nmeth.3707
Graves, a., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 38th International Conference on Acoustics, Speech, and Signal Processing, pp. 6645 – 6649 (2013). https://doi.org/10.1109/ICASSP.2013.6638947
Greff, K., Srivastava, R.K., Koutnik, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey (2016). https://doi.org/10.1109/TNNLS.2016.2582924
Groth, P., Gibson, A., Velterop, J.: The anatomy of a nanopublication. Inf. Serv. Use 30(1–2), 51–56 (2010). https://doi.org/10.3233/ISU-2010-0613
Groth, P., Loizou, A., Gray, A.J.G., Goble, C., Harland, L., Pettifer, S.: API-centric linked data integration: the open PHACTS discovery platform case study. J. Web Semant. 29, 12–18 (2014). https://doi.org/10.1016/j.websem.2014.03.003
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv:1207.0580
Hochreiter, S., Urgen Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 1398, 137–142 (1998). https://doi.org/10.1007/s13928716
Kilicoglu, H., Shin, D., Fiszman, M., Rosemblat, G., Rindflesch, T.C.: SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics 28(23), 3158–3160 (2012). https://doi.org/10.1093/bioinformatics/bts591
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP, pp. 1746–1751 (2014). https://doi.org/10.3115/v1/D14-1181. arXiv:1408.5882
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. Int. Conf. Learn. Represent. 2015, 1–15 (2015)
Kristal, A.R., Till, C., Platz, E.A., Song, X., King, I.B., Neuhouser, M.L., Ambrosone, C.B., Thompson, I.M.: Serum lycopene concentration and prostate cancer risk: results from the prostate cancer prevention trial. Cancer Epidemiol. Biomark. Prev. 20(4), 638–646 (2011). https://doi.org/10.1158/1055-9965.EPI-10-1221
Kuhn, T., Barbano, P.E., Nagy, M.L., Krauthammer, M.: Broadening the scope of nanopublications. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7882 LNCS, pp. 487–501 (2013). https://doi.org/10.1007/978-3-642-38288-8-33
Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: Proceedings of The 32nd international conference on machine learning vol. 37, pp. 957–966 (2015)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. International Conference on Machine Learning - ICML 2014, vol. 32, pp. 1188–1196 (2014). https://doi.org/10.1145/2740908.2742760
Manning, C.D., Raghavan, P.: An introduction to information retrieval (2009). https://doi.org/10.1109/LPT.2009.2020494. URLhttp://dspace.cusat.ac.in/dspace/handle/123456789/2538
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. Nips pp. 1–9 (2013). https://doi.org/10.1162/jmlr.2003.3.4-5.951
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the International Conference on Learning Representations (ICLR 2013) pp. 1–12 (2013). https://doi.org/10.1162/153244303322533223. arXiv:1301.3781v3.pdf
Mikolov, T., Yih, W.t., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of NAACL-HLT, June, pp. 746–751 (2013)
Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., Chen, J., Song, X., Ward, R.: Deep Sentence embedding using long short-term memory networks: analysis and application to information retrieval. IEEE/ACM Trans. Audio Speech and Language Process. 24(4), 694–707 (2016). https://doi.org/10.1109/TASLP.2016.2520371
Pele, O., Werman, M.: Fast and robust earth mover’s distances. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 460–467 (2009). https://doi.org/10.1109/ICCV.2009.5459199
Peleteiro, B., Lopes, C., Figueiredo, C., Lunet, N.: Salt intake and gastric cancer risk according to Helicobacter pylori infection, smoking, tumour site and histological type. British Journal of Cancer 104(1), 198–207 (2011). https://doi.org/10.1038/sj.bjc.6605993. URLhttp://www.nature.com/doifinder/10.1038/sj.bjc.6605993
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). https://doi.org/10.3115/v1/D14-1162. URLhttp://aclweb.org/anthology/D14-1162
Price, B.Y.S., Flach, P.A.: Computational support for academic peer review: a perspective from artificial intelligence. Commun. ACM 60(3), 70–79 (2017)
Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks pp. 45–50 (2010). https://doi.org/10.13140/2.1.2393.1847
Rindflesch, T.C., Fiszman, M.: The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J. Biomed. Inform. 36(6), 462–477 (2003). https://doi.org/10.1016/j.jbi.2003.11.003
Schoenfeld, J.D., Ioannidis, J.P.A.: Is everything we eat associated with cancer? A systematic cookbook review. Am. J. Clin. Nutr. 97(1), 127–134 (2013). https://doi.org/10.3945/ajcn.112.047142
Toulmin, S.: The uses of argument. Ethics 70(1), vi, 264 (1958). https://doi.org/10.2307/2183556
Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp 384–394 (2010)
Velterop, J.: Nanopublications: the future of coping with information overload. LOGOS: J. World Book Community 21, 3–4 (2010)
Verheij, B.: The toulmin argument model in artificial intelligence. In: Rahwan I (ed) Argumentation in Artificial Intelligence, pp. 219–238. Springer (2009). https://doi.org/10.1007/978-0-387-98197-0
Wang, P., Xu, J., Xu, B., Liu, C.l., Zhang, H., Wang, F., Hao, H.: Semantic clustering and convolutional neural network for short text categorization. In: Proceedings ACL 2015 pp. 352–357 (2015). https://doi.org/10.1016/j.neucom.2015.09.096
Zhang, Y., Wallace, B.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. In: Proceedings of the The 8th International Joint Conference on Natural Language Processing, pp. 253–263 (2017). arXiv:1510.03820
Zhao, J., Stockwell, T., Roemer, A., Chikritzhs, T., Bostwick, Dea: Is alcohol consumption a risk factor for prostate cancer? A systematic review and metaanalysis. BMC Cancer 16(1), 845 (2016). https://doi.org/10.1186/s12885-016-2891-z
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
González Pinto, J.M., Balke, WT. Assessing plausibility of scientific claims to support high-quality content in digital collections. Int J Digit Libr 21, 47–60 (2020). https://doi.org/10.1007/s00799-018-0256-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00799-018-0256-8