{"id":"https://openalex.org/W4320525799","doi":"https://doi.org/10.26599/bdma.2022.9020051","title":"Denoising Graph Inference Network for Document-Level Relation Extraction","display_name":"Denoising Graph Inference Network for Document-Level Relation Extraction","publication_year":2023,"publication_date":"2023-01-26","ids":{"openalex":"https://openalex.org/W4320525799","doi":"https://doi.org/10.26599/bdma.2022.9020051"},"language":"en","primary_location":{"id":"doi:10.26599/bdma.2022.9020051","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2022.9020051","pdf_url":"https://ieeexplore.ieee.org/ielx7/8254253/10026288/10026508.pdf","source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ieeexplore.ieee.org/ielx7/8254253/10026288/10026508.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100330268","display_name":"Hailin Wang","orcid":"https://orcid.org/0000-0001-7614-2003"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]},{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hailin Wang","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070150374","display_name":"Ke Qin","orcid":"https://orcid.org/0000-0001-6174-3877"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Qin","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081385896","display_name":"Guiduo Duan","orcid":"https://orcid.org/0000-0003-4448-6906"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiduo Duan","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070092987","display_name":"Guangchun Luo","orcid":"https://orcid.org/0000-0001-7330-2139"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangchun Luo","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100330268"],"corresponding_institution_ids":["https://openalex.org/I150229711","https://openalex.org/I204831749"],"apc_list":null,"apc_paid":null,"fwci":2.0221,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.88839602,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"6","issue":"2","first_page":"248","last_page":"262"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T10028","display_name":"Topic Modeling","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/T10181","display_name":"Natural Language Processing Techniques","score":0.9997000098228455,"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":0.998199999332428,"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":0.7793536186218262},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6875775456428528},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6272537708282471},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6117206811904907},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5867533683776855},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5717852711677551},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5676414966583252},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5634119510650635},{"id":"https://openalex.org/keywords/dijkstras-algorithm","display_name":"Dijkstra's algorithm","score":0.5258505940437317},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5015583038330078},{"id":"https://openalex.org/keywords/dependency-graph","display_name":"Dependency graph","score":0.4274613857269287},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2575910687446594},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2190365493297577},{"id":"https://openalex.org/keywords/shortest-path-problem","display_name":"Shortest path problem","score":0.2043304443359375},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.1839200258255005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7793536186218262},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6875775456428528},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6272537708282471},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6117206811904907},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5867533683776855},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5717852711677551},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5676414966583252},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5634119510650635},{"id":"https://openalex.org/C173870130","wikidata":"https://www.wikidata.org/wiki/Q8548","display_name":"Dijkstra's algorithm","level":4,"score":0.5258505940437317},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5015583038330078},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.4274613857269287},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2575910687446594},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2190365493297577},{"id":"https://openalex.org/C22590252","wikidata":"https://www.wikidata.org/wiki/Q1058754","display_name":"Shortest path problem","level":3,"score":0.2043304443359375},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.1839200258255005}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.26599/bdma.2022.9020051","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2022.9020051","pdf_url":"https://ieeexplore.ieee.org/ielx7/8254253/10026288/10026508.pdf","source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:40d4b8484f354074b650b0adcab82842","is_oa":true,"landing_page_url":"https://doaj.org/article/40d4b8484f354074b650b0adcab82842","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data Mining and Analytics, Vol 6, Iss 2, Pp 248-262 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.26599/bdma.2022.9020051","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2022.9020051","pdf_url":"https://ieeexplore.ieee.org/ielx7/8254253/10026288/10026508.pdf","source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4099999964237213}],"awards":[{"id":"https://openalex.org/G3759175880","display_name":null,"funder_award_id":"62176046","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7081544095","display_name":null,"funder_award_id":"U19A2059","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7513715502","display_name":null,"funder_award_id":"U19A2059,62176046","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4320525799.pdf","grobid_xml":"https://content.openalex.org/works/W4320525799.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1551842868","https://openalex.org/W2045738181","https://openalex.org/W2138627627","https://openalex.org/W2163362093","https://openalex.org/W2250249423","https://openalex.org/W2251293245","https://openalex.org/W2251454145","https://openalex.org/W2346452181","https://openalex.org/W2511964075","https://openalex.org/W2906003738","https://openalex.org/W2920198243","https://openalex.org/W2939618867","https://openalex.org/W2951309718","https://openalex.org/W2952179106","https://openalex.org/W2952768212","https://openalex.org/W2962859618","https://openalex.org/W2963020213","https://openalex.org/W2963021258","https://openalex.org/W2963862093","https://openalex.org/W2964193968","https://openalex.org/W2964217331","https://openalex.org/W2970771982","https://openalex.org/W2971221499","https://openalex.org/W2975998869","https://openalex.org/W2988125365","https://openalex.org/W3018931444","https://openalex.org/W3021224558","https://openalex.org/W3027691400","https://openalex.org/W3035053871","https://openalex.org/W3046375318","https://openalex.org/W3093891978","https://openalex.org/W3101327207","https://openalex.org/W3102663935","https://openalex.org/W3103836967","https://openalex.org/W3112076981","https://openalex.org/W3114733456","https://openalex.org/W3118018449","https://openalex.org/W3151782522","https://openalex.org/W3155073135","https://openalex.org/W3173229273","https://openalex.org/W3177599311","https://openalex.org/W3188999884","https://openalex.org/W4281749865","https://openalex.org/W4285607142","https://openalex.org/W4385245566","https://openalex.org/W4404784155","https://openalex.org/W6689876912","https://openalex.org/W6691842494","https://openalex.org/W6753145384","https://openalex.org/W6768134808","https://openalex.org/W6790620667"],"related_works":["https://openalex.org/W2805262146","https://openalex.org/W2327631927","https://openalex.org/W2093568763","https://openalex.org/W69297589","https://openalex.org/W1985166372","https://openalex.org/W4379517534","https://openalex.org/W2003096546","https://openalex.org/W4289354592","https://openalex.org/W2430210575","https://openalex.org/W2165069859"],"abstract_inverted_index":{"Relation":[0],"Extraction":[1],"(RE)":[2],"is":[3,39,75,157,183],"to":[4,60,94,146,162,185],"obtain":[5],"a":[6,15,20,23,52,62,86,105,125,142],"predefined":[7],"relation":[8],"type":[9],"of":[10,17,40,72,166],"two":[11],"entities":[12],"mentioned":[13],"in":[14,33,77],"piece":[16],"text,":[18,35],"e.g.,":[19],"sentence-level":[21,45],"or":[22],"document-level":[24,78,88],"text.":[25],"Most":[26],"existing":[27],"studies":[28],"suffer":[29],"from":[30,136],"the":[31,34,56,113,120,137,155,160,164,193],"noise":[32],"and":[36,115,152,190],"necessary":[37],"pruning":[38],"great":[41],"importance.":[42],"The":[43,177],"conventional":[44],"RE":[46],"task":[47],"addresses":[48],"this":[49,70,81],"issue":[50],"by":[51,111],"denoising":[53,73],"method":[54,74,182],"using":[55],"shortest":[57],"dependency":[58,65,117,189],"path":[59],"build":[61],"long-range":[63,97,187],"semantic":[64,98,188],"between":[66],"entity":[67,167],"pairs.":[68,168],"However,":[69],"kind":[71],"scarce":[76],"RE.":[79],"In":[80],"work,":[82],"we":[83],"explicitly":[84],"model":[85],"denoised":[87,127],"graph":[89],"based":[90],"on":[91,150,159,173],"linguistic":[92],"knowledge":[93],"capture":[95],"various":[96],"dependencies":[99],"among":[100],"entities.":[101],"We":[102,139,169],"first":[103],"formalize":[104],"Syntactic":[106],"Dependency":[107],"Tree":[108],"forest":[109],"(SDT-forest)":[110],"introducing":[112],"syntax":[114],"discourse":[116],"relation.":[118],"Then,":[119],"Steiner":[121,129],"tree":[122],"algorithm":[123],"extracts":[124],"mention-level":[126],"graph,":[128],"Graph":[130],"(SG),":[131],"removing":[132],"linguistically":[133],"irrelevant":[134],"words":[135],"SDT-forest.":[138],"then":[140],"devise":[141],"slide":[143],"residual":[144],"attention":[145],"highlight":[147],"word-level":[148],"evidence":[149,179],"text":[151],"SG.":[153],"Finally,":[154],"classification":[156,194],"established":[158],"SG":[161],"infer":[163],"relations":[165],"conduct":[170],"extensive":[171],"experiments":[172],"three":[174],"public":[175],"datasets.":[176],"results":[178],"that":[180],"our":[181],"beneficial":[184],"establish":[186],"can":[191],"improve":[192],"performance":[195],"with":[196],"longer":[197],"texts.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}