References
- Bornmann, L., Wray, K.B., & Haunschild, R. (2020). Citation concept analysis (CCA): a new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by exemplary case studies including classic books by Thomas S. Kuhn and Karl R. Popper. Scientometrics, 122(2), 1051–1074. doi:10.1007/s11192-019-03326-2
- Chen, C. (2020). A Glimpse of the First Eight Months of the COVID-19 Literature on Microsoft Academic Graph: Themes, Citation Contexts, and Uncertainties. Frontiers in Research Metrics and Analytics, 5, 607286–607286. doi:10.3389/frma.2020.607286
- Chen, C., Song, M., & Heo, G.E. (2018). A scalable and adaptive method for finding semantically equivalent cue words of uncertainty. Journal of Informetrics, 12(1), 158–180. doi:10.1016/j.joi.2017.12.004
- Chen, C., & Song, M. (2017). Visual Analytic Observatory of Scientific Knowledge. In: Representing Scientific Knowledge. Springer, Cham. doi:10.1007/978-3-319-62543-0_9
- Elkin, P.L., Carter, J.S., Nabar, M., Tuttle, M., Lincoln, M., & Brown, S.H. (2011). Drug knowledge expressed as computable semantic triples. Stud Health Technol Inform, 166, 38–47. doi:10.3233/978-1-60750-740-6-38
- Elsworth, B., & Gaunt, T.R. (2021). MELODI Presto: a fast and agile tool to explore semantic triples derived from biomedical literature. Bioinformatics, 37(4), 583–585. doi:10.1093/bioinformatics/btaa726
- Fabris, E., Kuhn, T., & Silvello, G. (2019). A Framework for Citing Nanopublications. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt, A. (eds) Digital Libraries for Open Knowledge. TPDL 2019. Lecture Notes in Computer Science, vol 11799. Springer, Cham. doi:10.1007/978-3-030-30760-8_6
- Fabris, E., Kuhn, T., & Silvello, G. (2020). Nanocitation: Complete and Interoperable Citations of Nanopublications. In: Ceci, M., Ferilli, S., Poggi, A. (eds) Digital Libraries: The Era of Big Data and Data Science. IRCDL 2020. Communications in Computer and Information Science, vol 1177. Springer, Cham. doi:10.1007/978-3-030-39905-4_18
- Vol. 1177 CCIS. Communications in Computer and Information Science (pp. 182–187).
- Flynn, A.J., Friedman, C.P., Boisvert, P., Landis-Lewis, Z., & Lagoze, C. (2018). The Knowledge Object Reference Ontology (KORO): A formalism to support management and sharing of computable biomedical knowledge for learning health systems. Learn Health Syst, 2(2), e10054. doi:10.1002/lrh2.10054
- Fortunato, S., Bergstrom, C.T., Boerner, K., Evans, J.A., Helbing, D., Milojevic, S., . . . Barabasi, A.-L. (2018). Science of science. Science, 359(6379). doi:10.1126/science.aao0185
- Friedman, C.P., & Flynn, A.J. (2019). Computable knowledge: An imperative for Learning Health Systems. Learn Health Syst, 3(4), e10203. doi:10.1002/lrh2.10203
- Groth, P., Gibson, A., & Velterop, J. (2010). The anatomy of a nanopublication. Information Services and Use, 30(1–2), 51–56. doi:10.3233/ISU-2010-0613
- Guo, X., Chen, Y., Du, J., & Dong, E. (2022). 259067 Subject-Predicate-Object triples extracted from scientific documents regarding cardiovascular research in China during 2000–2020. V2. Science Data Bank. [2022-04-01]. doi:10.11922/sciencedb.01660
- Herrera-perez, D., Haslam, A., Crain, T., Gill, J., Livingston, C., Kaestner, V., . . . Prasad, V. (2019). A comprehensive review of randomized clinical trials in three medical journals reveals 396 medical reversals. ELIFE, 8. doi:10.7554/eLife.45183
- Kilicoglu, H., Rosemblat, G., Fiszman, M., & Shin, D. (2020). Broad-coverage biomedical relation extraction with SemRep. BMC Bioinformatics, 21(1), 188. doi:10.1186/s12859-020-3517-7
- Kilicoglu, H., Rosemblat, G., & Rindflesch, T.C. (2017). Assigning factuality values to semantic relations extracted from biomedical research literature. PLoS One, 12(7), e0179926. doi:10.1371/journal.pone.0179926
- Kilicoglu, H., Shin, D., Fiszman, M., Rosemblat, G., & Rindflesch, T.C. (2012). SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics, 28(23), 3158–3160. doi:10.1093/bioinformatics/bts591
- Li, X., Peng, S., & Du, J. (2021). Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context. Scientometrics, 1–27. doi:10.1007/s11192-021-03880-8
- Malec, S.A., & Boyce, R.D. (2020). Exploring Novel Computable Knowledge in Structured Drug Product Labels. AMIA Jt Summits Transl Sci Proc, 2020, 403–412.
- Mons, B. (2019). FAIR Science for Social Machines: Let's Share Metadata Knowlets in the Internet of FAIR Data and Services. Data Intelligence, 1(1), 22–42. doi:10.1162/dint_a_00002
- Mons, B., van Haagen, H., Chichester, C., t Hoen, P.-B., den Dunnen, J.T., van Ommen, G., . . . Schultes, E. (2011). The value of data. Nature Genetics, 43(4), 281–283. doi:10.1038/ng0411-281
- Murray, D., Lamers, W., Boyack, K., Lariviere, V., Sugimoto, C.R., van Eck, N.J., & Waltman, L. (2019). Measuring disagreement in science. Proceedings of the 17th International Conference on Scientometrics & Informetrics (ISSI 2019), Vol. II.
- Rindflesch, T.C., & Fiszman, M. (2003). The interaction of domain knowledge and linguistic structure in natural language processing: Interpreting hypernymic propositions in biomedical text. Journal of Biomedical Informatics, 36(6), 462–477. doi:10.1016/j.jbi.2003.11.003
- Simpkin, A.L., & Schwartzstein, R.M. (2016). Tolerating Uncertainty—The Next Medical Revolution? New England Journal of Medicine, 375(18), 1713–1715. doi:10.1056/NEJMp1606402
- Small, H. (2020). Past as prologue: Approaches to the study of confirmation in science. Quantitative Science Studies, 1(3), 1025–1040. doi:10.1162/qss_a_00063
- Szarvas, G., Vincze, V., Farkas, R., Mora, G., & Gurevych, I. (2012). Cross-genre and cross-domain detection of semantic uncertainty. Computational Linguistics, 38(2), 335–367. doi:10.1162/COLI_a_00098
- van der Bles, A.M., van der Linden, S., Freeman, A.L.J., Mitchell, J., Galvao, A.B., Zaval, L., & Spiegelhalter, D.J. (2019). Communicating uncertainty about facts, numbers and science. Royal Society Open Science, 6(5). doi:10.1098/rsos.181870
- Williams, A.J., Harland, L., Groth, P., Pettifer, S., Chichester, C., Willighagen, E.L., . . . Mons, B. (2012). Open PHACTS: Semantic interoperability for drug discovery. Drug Discovery Today, 17(21–22), 1188–1198. doi:10.1016/j.drudis.2012.05.016
- Wyatt, J., & Scott, P. (2020). Computable knowledge is the enemy of disease. BMJ Health Care Inform, 27(2). doi:10.1136/bmjhci-2020-100200