References
- A reintroduction to our Knowledge Graph and knowledge panels. (2020). https://blog.google/products/search/about-knowledge-graph-and-knoswledge-panels/
- Ammar, W., Peters, M.E., Bhagavatula, C., & Power, R. (2017). The AI2 system at SemEval-2017 Task 10 (ScienceIE): Semi-supervised end-to-end entity and relation extraction. SemEval@ACL.
- Aryani, A., Poblet, M., Unsworth, K., Wang, J., Evans, B., Devaraju, A., Hausstein, B., Klas, C.-P., Zapilko, B., & Kaplun, S. (2018). A Research Graph dataset for connecting research data repositories using RD-Switchboard. Scientific Data, 5, 180099.
- Auer, S. (2018). Towards an Open Research Knowledge Graph (Version 1) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.1157185
- Augenstein, I., Das, M., Riedel, S., Vikraman, L., & McCallum, A. (2017). SemEval 2017 Task 10: ScienceIE—Extracting Keyphrases and Relations from Scientific Publications. SemEval@ACL.
- Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386.
- Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A pretrained language model for scientific text. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3606–3611.
- Birkle, C., Pendlebury, D.A., Schnell, J., & Adams, J. (2020). Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1(1), 363–376.
- Brack, A., D’Souza, J., Hoppe, A., Auer, S., & Ewerth, R. (2020). Domain-independent extraction of scientific concepts from research articles. European Conference on Information Retrieval, 251–266.
- Burton, A., Koers, H., Manghi, P., La Bruzzo, S., Aryani, A., Diepenbroek, M., & Schindler, U. (2017). The data-literature interlinking service: Towards a common infrastructure for sharing data-article links. Program: electronic library and information systems, 51(1), 75–100. https://doi.org/10.1108/PROG-06-2016-0048
- Buscaldi, D., Dessì, D., Motta, E., Osborne, F., & Reforgiato Recupero, D. (2019). Mining scholarly data for fine-grained knowledge graph construction. CEUR Workshop Proceedings, 2377, 21–30.
- Camacho-Collados, J., & Pilehvar, M.T. (2017). On the role of text preprocessing in neural network architectures: An evaluation study on text categorization and sentiment analysis. ArXiv Preprint ArXiv:1707.01780.
- Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. ArXiv:1406.1078.
- Cimiano, P., Mädche, A., Staab, S., & Völker, J. (2009). Ontology learning. In Handbook on ontologies (pp. 245–267). Springer.
- Constantin, A., Peroni, S., Pettifer, S., Shotton, D., & Vitali, F. (2016). The document components ontology (DoCO). Semantic Web, 7(2), 167–181.
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv:1810.04805.
- D’Souza, J., & Auer, S. (2020). NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature. In C. Zhang, P. Mayr, W. Lu, & Y. Zhang (Eds.), Proceedings of the 1st Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents co-located with the ACM/IEEE Joint Conference on Digital Libraries in 2020, EEKE@JCDL 2020, Virtual Event, China, August 1st, 2020 (Vol. 2658, pp. 16–27). CEUR-WS.org. http://ceur-ws.org/Vol-2658/paper2.pdf
- D’Souza, J., Hoppe, A., Brack, A., Jaradeh, M.Y., Auer, S., & Ewerth, R. (2020). The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. LREC, 2192–2203.
- Esteves, D., Moussallem, D., Neto, C.B., Soru, T., Usbeck, R., Ackermann, M., & Lehmann, J. (2015). MEX vocabulary: A lightweight interchange format for machine learning experiments. Proceedings of the 11th International Conference on Semantic Systems, 169–176.
- Fisas, B., Ronzano, F., & Saggion, H. (2016). A Multi-Layered Annotated Corpus of Scientific Papers. LREC.
- Fricke, S. (2018). Semantic scholar. Journal of the Medical Library Association: JMLA, 106(1), 145.
- Ghaddar, A., & Langlais, P. (2018). Robust lexical features for improved neural network named-entity recognition. ArXiv:1806.03489.
- GROBID. (2008). GitHub. https://github.com/kermitt2/grobid
- Handschuh, S., & QasemiZadeh, B. (2014). The ACL RD-TEC: a dataset for benchmarking terminology extraction and classification in computational linguistics. COLING 2014: 4th International Workshop on Computational Terminology.
- Hendricks, G., Tkaczyk, D., Lin, J., & Feeney, P. (2020). Crossref: The sustainable source of community-owned scholarly metadata. Quantitative Science Studies, 1(1), 414–427.
- Huth, E.J. (1987). Structured abstracts for papers reporting clinical trials. American College of Physicians.
- Jaradeh, M.Y., Oelen, A., Farfar, K.E., Prinz, M., D’Souza, J., Kismihók, G., Stocker, M., & Auer, S. (2019). Open Research Knowledge Graph: Next Generation Infrastructure for Semantic Scholarly Knowledge. KCAP, 243–246.
- Jiang, M., D’Souza, J., Auer, S., & Downie, J.S. (2020). Targeting Precision: A Hybrid Scientific Relation Extraction Pipeline for Improved Scholarly Knowledge Organization. Proceedings of the Association for Information Science and Technology, 57(1).
- Jinha, A.E. (2010). Article 50 million: An estimate of the number of scholarly articles in existence. Learned Publishing, 23(3), 258–263.
- Johnson, R., Watkinson, A., & Mabe, M. (2018). The STM report. An Overview of Scientific and Scholarly Publishing. 5th Edition October.
- Kononova, O., Huo, H., He, T., Rong, Z., Botari, T., Sun, W., Tshitoyan, V., & Ceder, G. (2019). Text-mined dataset of inorganic materials synthesis recipes. Scientific Data, 6(1), 1–11.
- Kulkarni, C., Xu, W., Ritter, A., & Machiraju, R. (2018). An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols. NAACL: HLT, Volume 2 (Short Papers), 97–106. https://doi.org/10.18653/v1/N18-2016
- Kuniyoshi, F., Makino, K., Ozawa, J., & Miwa, M. (2020). Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature. LREC, 1941–1950.
- Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural architectures for named entity recognition. ArXiv Preprint ArXiv:1603.01360.
- Landhuis, E. (2016). Scientific literature: Information overload. Nature, 535(7612), 457–458.
- Liakata, M., Saha, S., Dobnik, S., Batchelor, C., & Rebholz-Schuhmann, D. (2012). Automatic recognition of conceptualization zones in scientific articles and two life science applications. Bioinformatics, 28(7), 991–1000.
- Liakata, M., Teufel, S., Siddharthan, A., & Batchelor, C.R. (2010). Corpora for the Conceptualisation and Zoning of Scientific Papers. LREC.
- Lin, D.K., & Pantel, P. (2002). Concept discovery from text. COLING 2002: The 19th International Conference on Computational Linguistics.
- Luan, Y., He, L., Ostendorf, M., & Hajishirzi, H. (2018). Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction. EMNLP.
- Luan, Y., Ostendorf, M., & Hajishirzi, H. (2017). Scientific information extraction with semi-supervised neural tagging. ArXiv:1708.06075.
- Mysore, S., Jensen, Z., Kim, E., Huang, K., Chang, H.-S., Strubell, E., Flanigan, J., McCallum, A., & Olivetti, E. (2019). The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures. Proceedings of the 13th Linguistic Annotation Workshop, 56–64.
- Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019). Industry-scale knowledge graphs: Lessons and challenges. Queue, 17(2), 48–75.
- Oelen, A., Jaradeh, M.Y., Farfar, K.E., Stocker, M., & Auer, S. (2019). Comparing research contributions in a scholarly knowledge graph. CEUR Workshop Proceedings 2526 (2019), 2526, 21–26.
- Oelen, A., Jaradeh, M.Y., Stocker, M., & Auer, S. (2020). Generate FAIR Literature Surveys with Scholarly Knowledge Graphs. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, 97–106. https://doi.org/10.1145/3383583.3398520
- Pertsas, V., & Constantopoulos, P. (2017). Scholarly Ontology: Modelling scholarly practices. International Journal on Digital Libraries, 18(3), 173–190.
- Qi, P., Zhang, Y.H., Zhang, Y.H., Bolton, J., & Manning, C.D. (2020). Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. https://nlp.stanford.edu/pubs/qi2020stanza.pdf
- Soldatova, L.N., & King, R.D. (2006). An ontology of scientific experiments. Journal of the Royal Society, Interface, 3 11, 795–803.
- Sollaci, L.B., & Pereira, M.G. (2004). The introduction, methods, results, and discussion (IMRAD) structure: A fifty-year survey. Journal of the Medical Library Association, 92(3), 364.
- Teufel, S., Carletta, J., & Moens, M. (1999). An annotation scheme for discourse-level argumentation in research articles. Proceedings of the Ninth Conference on European Chapter of ACL, 110–117.
- Teufel, S., Siddharthan, A., & Batchelor, C. (2009). Towards discipline-independent argumentative zoning: Evidence from chemistry and computational linguistics. EMNLP: Volume 3, 1493–1502.
- Vogt, L., D’Souza, J., Stocker, M., & Auer, S. (2020). Toward representing research contributions in scholarly knowledge graphs using knowledge graph cells. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, 107–116.
- Vrandečić, D., & Krötzsch, M. (2014). Wikidata: A free collaborative knowledgebase. Communications of the ACM, 57(10), 78–85.
- Wang, B.L., Lu, W., Wang, Y., & Jin, H.X. (2018). A neural transition-based model for nested mention recognition. ArXiv:1810.01808.
- Wang, K.S., Shen, Z.H., Huang, C.Y., Wu, C.-H., Dong, Y.X., & Kanakia, A. (2020). Microsoft academic graph: When experts are not enough. Quantitative Science Studies, 1(1), 396–413.
- Wilkinson, M.D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L.B., Bourne, P.E., & others. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 1–9.
- Zhou, J., Cao, Y., Wang, X.G., Li, P., & Xu, W. (2016). Deep recurrent models with fast-forward connections for neural machine translation. Transactions of the Association for Computational Linguistics, 4, 371–383.