{"id":"https://openalex.org/W4407108563","doi":"https://doi.org/10.1007/s44196-025-00738-2","title":"Hyperbolic Graph Convolutional Network Relation Extraction Model Combining Dependency Syntax and Contrastive Learning","display_name":"Hyperbolic Graph Convolutional Network Relation Extraction Model Combining Dependency Syntax and Contrastive Learning","publication_year":2025,"publication_date":"2025-02-03","ids":{"openalex":"https://openalex.org/W4407108563","doi":"https://doi.org/10.1007/s44196-025-00738-2"},"language":"en","primary_location":{"id":"doi:10.1007/s44196-025-00738-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44196-025-00738-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44196-025-00738-2.pdf","source":{"id":"https://openalex.org/S190680769","display_name":"International Journal of Computational Intelligence Systems","issn_l":"1875-6883","issn":["1875-6883","1875-6891"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44196-025-00738-2.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028609396","display_name":"Jinzhe Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161699","display_name":"Aviation General Hospital","ror":"https://ror.org/04j1qx617","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210161699"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinzhe Li","raw_affiliation_strings":["Information Center, Civil Aviation General Hospital, Chaoyanglu, Beijing, 100123, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Center, Civil Aviation General Hospital, Chaoyanglu, Beijing, 100123, China","institution_ids":["https://openalex.org/I4210161699"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5028609396"],"corresponding_institution_ids":["https://openalex.org/I4210161699"],"apc_list":{"value":1390,"currency":"GBP","value_usd":1704},"apc_paid":{"value":1390,"currency":"GBP","value_usd":1704},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00723277,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.9988999962806702,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7423242926597595},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.725687563419342},{"id":"https://openalex.org/keywords/syntax","display_name":"Syntax","score":0.5852490663528442},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5668314695358276},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5585120916366577},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5505164861679077},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5312620997428894},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.5222374200820923},{"id":"https://openalex.org/keywords/abstract-syntax","display_name":"Abstract syntax","score":0.45764464139938354},{"id":"https://openalex.org/keywords/dependency-grammar","display_name":"Dependency grammar","score":0.44297581911087036},{"id":"https://openalex.org/keywords/dependency-graph","display_name":"Dependency graph","score":0.442878395318985},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.365475058555603},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.24158790707588196},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.18226468563079834}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7423242926597595},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.725687563419342},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.5852490663528442},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5668314695358276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5585120916366577},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5505164861679077},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5312620997428894},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.5222374200820923},{"id":"https://openalex.org/C114408938","wikidata":"https://www.wikidata.org/wiki/Q333373","display_name":"Abstract syntax","level":3,"score":0.45764464139938354},{"id":"https://openalex.org/C164883195","wikidata":"https://www.wikidata.org/wiki/Q674834","display_name":"Dependency grammar","level":3,"score":0.44297581911087036},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.442878395318985},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.365475058555603},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.24158790707588196},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.18226468563079834}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44196-025-00738-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44196-025-00738-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44196-025-00738-2.pdf","source":{"id":"https://openalex.org/S190680769","display_name":"International Journal of Computational Intelligence Systems","issn_l":"1875-6883","issn":["1875-6883","1875-6891"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8f660c0966b245048e8f5ceeabb1336f","is_oa":false,"landing_page_url":"https://doaj.org/article/8f660c0966b245048e8f5ceeabb1336f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Computational Intelligence Systems, Vol 18, Iss 1, Pp 1-25 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44196-025-00738-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44196-025-00738-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44196-025-00738-2.pdf","source":{"id":"https://openalex.org/S190680769","display_name":"International Journal of Computational Intelligence Systems","issn_l":"1875-6883","issn":["1875-6883","1875-6891"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4407108563.pdf","grobid_xml":"https://content.openalex.org/works/W4407108563.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W2099779943","https://openalex.org/W2144578941","https://openalex.org/W2162590473","https://openalex.org/W2513378248","https://openalex.org/W2517194566","https://openalex.org/W2759211898","https://openalex.org/W2892094955","https://openalex.org/W2951525151","https://openalex.org/W2952768212","https://openalex.org/W2963341956","https://openalex.org/W2964167098","https://openalex.org/W2964212344","https://openalex.org/W2964217331","https://openalex.org/W2982490267","https://openalex.org/W2990138404","https://openalex.org/W2996917304","https://openalex.org/W3034891697","https://openalex.org/W3105816068","https://openalex.org/W3116238564","https://openalex.org/W3116453892","https://openalex.org/W3174505712","https://openalex.org/W3175362188","https://openalex.org/W3176214425","https://openalex.org/W3208152475","https://openalex.org/W4246477479","https://openalex.org/W4285138368","https://openalex.org/W4312546551","https://openalex.org/W4385571437","https://openalex.org/W4389431446","https://openalex.org/W4389523710","https://openalex.org/W4392214409","https://openalex.org/W4392240262","https://openalex.org/W4402138326","https://openalex.org/W4404278385","https://openalex.org/W4404784155"],"related_works":["https://openalex.org/W2098784136","https://openalex.org/W4241489294","https://openalex.org/W63925617","https://openalex.org/W2252142543","https://openalex.org/W122365991","https://openalex.org/W2151754849","https://openalex.org/W3123290982","https://openalex.org/W3153965608","https://openalex.org/W2405117110","https://openalex.org/W4285169662"],"abstract_inverted_index":{"In":[0],"current":[1],"relation":[2,48,155,219],"extraction":[3,156,220],"tasks,":[4],"when":[5],"the":[6,12,56,59,90,101,120,136,145,154,165,172,187,195,207,216],"input":[7],"sentence":[8,35,233],"structure":[9,45],"is":[10,23,162],"complex,":[11],"performance":[13,217],"of":[14,28,81,138,147,218],"in-context":[15,237],"learning":[16,160],"methods":[17],"based":[18],"on":[19,119,153,221,229],"large":[20],"language":[21,66],"model":[22,91,166,196,209],"still":[24],"lower":[25],"than":[26],"that":[27,206],"traditional":[29,70],"pre-train":[30],"fine-tune":[31],"models.":[32],"For":[33],"complex":[34,232],"structures,":[36],"dependency":[37,150,174,178,201],"syntax":[38,151,175,179],"information":[39,46,61,127,129,152,176,180,189],"can":[40,213],"provide":[41],"effective":[42],"prior":[43],"text":[44],"for":[47,84],"extraction.":[49],"However,":[50],"most":[51],"studies":[52],"are":[53,132],"affected":[54],"by":[55,64],"noise":[57,148,199],"in":[58,149,171,197,200,210],"syntactic":[60,126],"automatically":[62],"extracted":[63],"natural":[65],"processing":[67],"toolkits.":[68],"Additionally,":[69],"pre-training":[71],"encoders":[72],"have":[73],"issues":[74],"such":[75],"as":[76],"an":[77],"overly":[78],"centralized":[79],"representation":[80],"word":[82,139],"embedding":[83,140],"high-frequency":[85],"words,":[86],"which":[87],"adversely":[88],"affects":[89],"to":[92,134,143,193,236],"learn":[93],"contextual":[94],"semantic":[95],"information.":[96],"To":[97],"address":[98],"proposed":[99,208],"problem,":[100],"paper":[102,212],"proposes":[103],"a":[104,158],"Hyperbolic":[105],"Graph":[106],"Convolutional":[107],"Network":[108],"Relation":[109],"Extraction":[110],"Model":[111],"Combine":[112],"Dependency":[113],"Syntax":[114],"and":[115,128,177],"Contrastive":[116],"Learning.":[117],"Based":[118],"hyperbolic":[121],"graph":[122],"neural":[123],"network,":[124],"dependent":[125],"optimization":[130],"strategies":[131],"introduced":[133],"solve":[135],"problem":[137],"concentration.":[141],"Simultaneously,":[142],"mitigate":[144],"impact":[146],"task,":[157],"contrastive":[159],"approach":[161],"employed.":[163],"After":[164],"learns":[167],"context":[168],"semantics":[169],"separately":[170],"original":[173],"with":[181,231],"added":[182],"random":[183],"noise,":[184],"it":[185],"maximizes":[186],"mutual":[188],"between":[190],"entity":[191],"words":[192],"assist":[194],"distinguishing":[198],"syntax.":[202],"The":[203],"experiments":[204],"indicate":[205],"this":[211],"effectively":[214],"enhance":[215],"public":[222],"datasets,":[223],"especially":[224],"achieving":[225],"significantly":[226],"higher":[227],"precision":[228],"datasets":[230],"structures":[234],"compared":[235],"learning.":[238]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
