{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:44:55Z","timestamp":1772793895143,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031778469","type":"print"},{"value":"9783031778476","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-77847-6_8","type":"book-chapter","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T04:29:24Z","timestamp":1732595364000},"page":"134-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SciHyp: A Fine-Grained Dataset Describing Hypotheses and\u00a0Their Components from\u00a0Scientific Articles"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0302-5065","authenticated-orcid":false,"given":"Rosni","family":"Vasu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2076-9584","authenticated-orcid":false,"given":"Cristina","family":"Sarasua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0128-4602","authenticated-orcid":false,"given":"Abraham","family":"Bernstein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"8_CR1","unstructured":"Achiam, J., et\u00a0al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"issue":"4","key":"8_CR2","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1162\/coli.07-034-R2","volume":"34","author":"R Artstein","year":"2008","unstructured":"Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Comput. Linguist. 34(4), 555\u2013596 (2008)","journal-title":"Comput. Linguist."},{"issue":"16","key":"8_CR3","doi-asserted-by":"publisher","DOI":"10.3346\/jkms.2022.37.e121","volume":"37","author":"E Barroga","year":"2022","unstructured":"Barroga, E., Matanguihan, G.J.: A practical guide to writing quantitative and qualitative research questions and hypotheses in scholarly articles. J. Korean Med. Sci. 37(16), e121 (2022)","journal-title":"J. Korean Med. Sci."},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3615\u20133620 (2019)","DOI":"10.18653\/v1\/D19-1371"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Brinner, M., Heger, T., Zarriess, S.: Linking a hypothesis network from the domain of invasion biology to a corpus of scientific abstracts: the INAS dataset. In: Proceedings of the first Workshop on Information Extraction from Scientific Publication, pp. 32\u201342. Association for Computational Linguistics, Online (2022). https:\/\/aclanthology.org\/2022.wiesp-1.5","DOI":"10.18653\/v1\/2022.wiesp-1.5"},{"key":"8_CR6","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Bucur, C.I., Kuhn, T., Ceolin, D., van Ossenbruggen, J.: Expressing high-level scientific claims with formal semantics. In: Proceedings of the 11th on Knowledge Capture Conference, pp. 233\u2013240 (2021)","DOI":"10.1145\/3460210.3493561"},{"key":"8_CR8","unstructured":"Chen, V.Z., Montano-Campos, F., Zadrozny, W., Canfield, E.: Machine reading of hypotheses for organizational research reviews and pre-trained models via R shiny app for non-programmers. arXiv preprint arXiv:2106.16102 (2021)"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Cho, H., et al.: Prompt-augmented linear probing: scaling beyond the limit of few-shot in-context learners. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 12709\u201312718 (2023)","DOI":"10.1609\/aaai.v37i11.26495"},{"issue":"1","key":"8_CR10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37\u201346 (1960)","journal-title":"Educ. Psychol. Measur."},{"issue":"4","key":"8_CR11","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.1162\/qss_a_00161","volume":"2","author":"M F\u00e4rber","year":"2021","unstructured":"F\u00e4rber, M., Lamprecht, D.: The data set knowledge graph: creating a linked open data source for data sets. Quant. Sci. Stud. 2(4), 1324\u20131355 (2021)","journal-title":"Quant. Sci. Stud."},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"F\u00e4rber, M., Lamprecht, D., Krause, J., Aung, L., Haase, P.: SemOpenAlex: the scientific landscape in 26 billion RDF triples. In: Payne, T.R., et al (eds.) International Semantic Web Conference, vol. 14266, pp. 94\u2013112. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-47243-5_6","DOI":"10.1007\/978-3-031-47243-5_6"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"F\u00e4rber, M., Nishioka, C., Jatowt, A.: ScholarSight: visualizing temporal trends of scientific concepts. In: 2019 ACM\/IEEE Joint Conference on Digital Libraries (JCDL), pp. 438\u2013439. IEEE (2019)","DOI":"10.1109\/JCDL.2019.00108"},{"issue":"5","key":"8_CR14","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1037\/h0062427","volume":"35","author":"JN Farr","year":"1951","unstructured":"Farr, J.N., Jenkins, J.J., Paterson, D.G.: Simplification of flesch reading ease formula. J. Appl. Psychol. 35(5), 333 (1951)","journal-title":"J. Appl. Psychol."},{"key":"8_CR15","unstructured":"Garijo, D., Gil, Y., Ratnakar, V.: The disk hypothesis ontology: capturing hypothesis evolution for automated discovery. In: K-CAP Workshops, pp. 40\u201346 (2017)"},{"key":"8_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/978-3-030-77385-4_28","volume-title":"The Semantic Web","author":"R de Haan","year":"2021","unstructured":"de Haan, R., Tiddi, I., Beek, W.: Discovering research hypotheses in social science using knowledge graph embeddings. In: Verborgh, R., et al. (eds.) ESWC 2021. LNCS, vol. 12731, pp. 477\u2013494. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77385-4_28"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Han, X., Simig, D., Mihaylov, T., Tsvetkov, Y., Celikyilmaz, A., Wang, T.: Understanding in-context learning via supportive pretraining data. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 12660\u201312673 (2023)","DOI":"10.18653\/v1\/2023.acl-long.708"},{"issue":"1","key":"8_CR18","doi-asserted-by":"publisher","first-page":"51","DOI":"10.2466\/pr0.1984.55.1.51","volume":"55","author":"CW Hess","year":"1984","unstructured":"Hess, C.W., Ritchie, K.P., Landry, R.G.: The type-token ratio and vocabulary performance. Psychol. Rep. 55(1), 51\u201357 (1984)","journal-title":"Psychol. Rep."},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Jaradeh, M.Y., Oelen, A., Prinz, M., Stocker, M., Auer, S.: Open research knowledge graph: a system walkthrough. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt A., (eds.) Digital Libraries for Open Knowledge: 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Oslo, Norway, 9-12 September 2019, Proceedings 23, pp. 348\u2013351. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30760-8","DOI":"10.1007\/978-3-030-30760-8"},{"key":"8_CR20","unstructured":"Kenton, J.D.M.W.C., Toutanova, L.K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, vol.\u00a01, p.\u00a02 (2019)"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Ko, W.J., Durrett, G., Li, J.J.: Domain agnostic real-valued specificity prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 6610\u20136617 (2019)","DOI":"10.1609\/aaai.v33i01.33016610"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Liu, J., Shen, D., Zhang, Y., Dolan, W.B., Carin, L., Chen, W.: What makes good in-context examples for GPT-3? In: Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pp. 100\u2013114 (2022)","DOI":"10.18653\/v1\/2022.deelio-1.10"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Lo, K., Wang, L.L., Neumann, M., Kinney, R., Weld, D.S.: S2ORC: the semantic scholar open research corpus. arXiv preprint arXiv:1911.02782 (2019)","DOI":"10.18653\/v1\/2020.acl-main.447"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Mueller, R., Abdullaev, S.: DeepCause: hypothesis extraction from information systems papers with deep learning for theory ontology learning. In: Proceedings of the 52nd Hawaii International Conference on System Sciences (2019)","DOI":"10.24251\/HICSS.2019.752"},{"key":"8_CR25","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1162\/tacl_a_00040","volume":"6","author":"S Paun","year":"2018","unstructured":"Paun, S., Carpenter, B., Chamberlain, J., Hovy, D., Kruschwitz, U., Poesio, M.: Comparing Bayesian models of annotation. Trans. Assoc. Comput. Linguist. 6, 571\u2013585 (2018)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Pei, J., et al.: Potato: the portable text annotation tool. In: Proceedings of the The 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 327\u2013337 (2022)","DOI":"10.18653\/v1\/2022.emnlp-demos.33"},{"issue":"1","key":"8_CR27","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1038\/s41467-024-45914-8","volume":"15","author":"MP Polak","year":"2024","unstructured":"Polak, M.P., Morgan, D.: Extracting accurate materials data from research papers with conversational language models and prompt engineering. Nat. Commun. 15(1), 1569 (2024)","journal-title":"Nat. Commun."},{"issue":"2","key":"8_CR28","doi-asserted-by":"publisher","first-page":"7","DOI":"10.4018\/ijswis.2014040102","volume":"10","author":"M Poveda-Villal\u00f3n","year":"2014","unstructured":"Poveda-Villal\u00f3n, M., G\u00f3mez-P\u00e9rez, A., Su\u00e1rez-Figueroa, M.C.: Oops!(ontology pitfall scanner!): an on-line tool for ontology evaluation. Int. J. Semant. Web Inf. Syst. (IJSWIS) 10(2), 7\u201334 (2014)","journal-title":"Int. J. Semant. Web Inf. Syst. (IJSWIS)"},{"key":"8_CR29","unstructured":"Reklos, I., Mero\u00f1o-Pe\u00f1uela, A.: Medicause: causal relation modelling and extraction from medical publications. In: CEUR Workshop Proceedings, vol.\u00a03184, pp. 1\u201318. CEUR-WS (2022)"},{"issue":"1","key":"8_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-018-0639-1","volume":"18","author":"M Shardlow","year":"2018","unstructured":"Shardlow, M., Batista-Navarro, R., Thompson, P., Nawaz, R., McNaught, J., Ananiadou, S.: Identification of research hypotheses and new knowledge from scientific literature. BMC Med. Inform. Decis. Mak. 18(1), 1\u201313 (2018)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"8_CR31","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"8_CR32","doi-asserted-by":"publisher","unstructured":"Soldatova, L.N., Rzhetsky, A.: Representation of research hypotheses. In: J. Biomed. Semant. 2, 1\u201315 (2011). https:\/\/doi.org\/10.1186\/2041-1480-2-S2-S9","DOI":"10.1186\/2041-1480-2-S2-S9"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Swanson, R., Ecker, B., Walker, M.: Argument mining: extracting arguments from online dialogue. In: Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 217\u2013226 (2015)","DOI":"10.18653\/v1\/W15-4631"},{"issue":"8","key":"8_CR34","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.230607","volume":"10","author":"WH Thompson","year":"2023","unstructured":"Thompson, W.H., Skau, S.: On the scope of scientific hypotheses. Royal Soc. Open Sci. 10(8), 230607 (2023)","journal-title":"Royal Soc. Open Sci."},{"key":"8_CR35","unstructured":"Touvron, H., et\u00a0al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"8_CR36","unstructured":"Vargas, H., Garijo, D., Gil, Y.: The scientific questions ontology (2017). https:\/\/w3id.org\/sqo\/1.3.1\/, revision: v1.3.1"},{"issue":"5","key":"8_CR37","first-page":"360","volume":"37","author":"AJ Viera","year":"2005","unstructured":"Viera, A.J., Garrett, J.M., et al.: Understanding interobserver agreement: the kappa statistic. Fam. Med. 37(5), 360\u2013363 (2005)","journal-title":"Fam. Med."},{"issue":"3","key":"8_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (csur) 53(3), 1\u201334 (2020)","journal-title":"ACM Comput. Surv. (csur)"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77847-6_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T15:53:02Z","timestamp":1738425182000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77847-6_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"ISBN":["9783031778469","9783031778476"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77847-6_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"27 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanover, MD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2024.semanticweb.org\/event\/3715c6fc-e2d7-47eb-8c01-5fe4ac589a52\/summary","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}