{"id":"https://openalex.org/W4411549679","doi":"https://doi.org/10.1145/3701716.3717752","title":"Using Large Language Models for Hypotheses and Claims Extraction from Scientific Literature","display_name":"Using Large Language Models for Hypotheses and Claims Extraction from Scientific Literature","publication_year":2025,"publication_date":"2025-05-08","ids":{"openalex":"https://openalex.org/W4411549679","doi":"https://doi.org/10.1145/3701716.3717752"},"language":"en","primary_location":{"id":"doi:10.1145/3701716.3717752","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3717752","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3717752","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3717752","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010048488","display_name":"Sagar Uprety","orcid":"https://orcid.org/0000-0001-7858-6265"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sagar Uprety","raw_affiliation_strings":["University College London, London, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0001-7858-6265","affiliations":[{"raw_affiliation_string":"University College London, London, United Kingdom","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075694387","display_name":"Boyana Buyuklieva","orcid":"https://orcid.org/0000-0002-3583-2080"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Boyana Buyuklieva","raw_affiliation_strings":["University College London, London, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-3583-2080","affiliations":[{"raw_affiliation_string":"University College London, London, United Kingdom","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082046789","display_name":"Prayag Tiwari","orcid":"https://orcid.org/0000-0002-2851-4260"},"institutions":[{"id":"https://openalex.org/I746986","display_name":"Halmstad University","ror":"https://ror.org/03h0qfp10","country_code":"SE","type":"education","lineage":["https://openalex.org/I746986"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Prayag Tiwari","raw_affiliation_strings":["Halmstad University, Halmstad, Sweden"],"raw_orcid":"https://orcid.org/0000-0002-2851-4260","affiliations":[{"raw_affiliation_string":"Halmstad University, Halmstad, Sweden","institution_ids":["https://openalex.org/I746986"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11774776,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1645","last_page":"1648"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6715911030769348},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5674357414245605},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4532720148563385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3995123505592346},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.3790414035320282},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3481874465942383},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.11490637063980103},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06675803661346436}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6715911030769348},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5674357414245605},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4532720148563385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3995123505592346},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3790414035320282},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3481874465942383},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.11490637063980103},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06675803661346436},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3701716.3717752","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3717752","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3717752","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3701716.3717752","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3717752","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3717752","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6899999976158142,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411549679.pdf","grobid_xml":"https://content.openalex.org/works/W4411549679.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W2250984714","https://openalex.org/W3170447327","https://openalex.org/W3199024427","https://openalex.org/W4366418577","https://openalex.org/W4403749653","https://openalex.org/W6600103761","https://openalex.org/W6600297362","https://openalex.org/W6601700763","https://openalex.org/W6832806024"],"related_works":["https://openalex.org/W2377297411","https://openalex.org/W3148217948","https://openalex.org/W2375788636","https://openalex.org/W2358561207","https://openalex.org/W2975617233","https://openalex.org/W2388704129","https://openalex.org/W2392827053","https://openalex.org/W3188962172","https://openalex.org/W2377877252","https://openalex.org/W3204019825"],"abstract_inverted_index":{"We":[0,20],"present":[1],"a":[2,51],"novel":[3],"dataset":[4,29],"for":[5,53],"hypothesis":[6,54],"and":[7,33,46,55],"claim":[8,56],"extraction":[9],"from":[10],"research":[11],"papers":[12],"in":[13],"the":[14,28],"interdisciplinary":[15],"domain":[16],"of":[17],"social":[18],"infertility.":[19],"use":[21,43],"OpenAI's":[22],"o1-mini":[23],"reasoning":[24],"LLM":[25],"to":[26,49],"create":[27,50],"by":[30],"extracting":[31],"hypotheses":[32],"claims,":[34],"which":[35],"are":[36],"then":[37],"manually":[38],"validated.":[39],"Then":[40],"we":[41],"also":[42],"smaller":[44],"closed":[45],"open-weight":[47],"LLMs":[48],"benchmark":[52],"extraction.":[57]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}