{"id":"https://openalex.org/W7125971409","doi":"https://doi.org/10.1109/smc58881.2025.11342532","title":"CNRel: Candidate Prompt Enhancement and Noise Filtering Relational Triple Extraction Framework Based on Large Language Models","display_name":"CNRel: Candidate Prompt Enhancement and Noise Filtering Relational Triple Extraction Framework Based on Large Language Models","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125971409","doi":"https://doi.org/10.1109/smc58881.2025.11342532"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11342532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342532","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124080550","display_name":"Wei Li","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I4210156404","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124059094","display_name":"Pan Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pan Xie","raw_affiliation_strings":["China United Network Communications Group Company LTD,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China United Network Communications Group Company LTD,China","institution_ids":["https://openalex.org/I6507939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124084577","display_name":"Chenbin Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenbin Zhao","raw_affiliation_strings":["China United Network Communications Group Company LTD,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China United Network Communications Group Company LTD,China","institution_ids":["https://openalex.org/I6507939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065859286","display_name":"Hui Li","orcid":"https://orcid.org/0000-0001-9198-3951"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Li","raw_affiliation_strings":["Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I4210156404","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065586298","display_name":"Liangxiong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangxiong Li","raw_affiliation_strings":["Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I4210156404","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074564411","display_name":"Jingguo Ge","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingguo Ge","raw_affiliation_strings":["Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Institute of Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I4210156404","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.80518448,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7592","last_page":"7597"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.5508000254631042,"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":0.5508000254631042,"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":0.24560000002384186,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.046799998730421066,"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/noise","display_name":"Noise (video)","score":0.6406999826431274},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5898000001907349},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.49959999322891235},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4862000048160553},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4593999981880188},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4449000060558319},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40130001306533813},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.39250001311302185},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.3815000057220459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7813000082969666},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6406999826431274},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5898000001907349},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.541700005531311},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.49959999322891235},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4862000048160553},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.450300008058548},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44620001316070557},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3815000057220459},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.3531999886035919},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34929999709129333},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.3440000116825104},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3325999975204468},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.27790001034736633},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11342532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342532","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6654970645904541,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2134033474","https://openalex.org/W2741956709","https://openalex.org/W2798734500","https://openalex.org/W2808142148","https://openalex.org/W2964167098","https://openalex.org/W2998446468","https://openalex.org/W3003265726","https://openalex.org/W3034617555","https://openalex.org/W3116427155","https://openalex.org/W3176398467","https://openalex.org/W4385572217","https://openalex.org/W4401754921","https://openalex.org/W4404783098"],"related_works":[],"abstract_inverted_index":{"Relational":[0,85],"Triple":[1,86],"Extraction":[2,87],"(RTE)":[3],"focuses":[4],"on":[5,90,116,194],"extracting":[6],"triples":[7,30,166,176],"from":[8,31],"sentences,":[9],"a":[10,107,117,144,214],"crucial":[11],"task":[12],"in":[13,130],"the":[14,25,41,50,68,131,134,155,158,174,181,187,190,220,226],"automatic":[15],"construction":[16],"of":[17,70,136,189,222],"knowledge":[18,60],"graphs.":[19],"Large":[20,91],"Language":[21,92],"Models":[22,93],"(LLMs)":[23],"have":[24],"ability":[26],"to":[27,40,122,153,218,225],"automatically":[28],"extract":[29,123,163],"text":[32],"through":[33,177],"appropriate":[34],"instructions":[35],"or":[36],"fine-tuning.":[37],"However,":[38],"due":[39],"bias":[42],"between":[43,157],"LLMs":[44,146],"training":[45],"data":[46],"and":[47,61,82,101,112,124,162,179,211],"inference":[48],"data,":[49],"previous":[51,205],"LLM-based":[52],"triple":[53,208],"extraction":[54,111,209],"method":[55],"ignores":[56],"many":[57,165],"potentially":[58],"valuable":[59,139],"lacks":[62],"noise":[63],"filtering,":[64],"which":[65,95,184],"greatly":[66,185],"limits":[67],"capability":[69],"RTE":[71,191],"model.":[72,192],"To":[73],"address":[74],"these":[75],"challenges,":[76],"we":[77,104,212],"propose":[78],"Candidate":[79],"Prompt":[80],"Enhancement":[81],"Noise":[83,170],"Filtering":[84],"Framework":[88],"Based":[89],"(CNRel),":[94],"combines":[96],"small":[97,118],"pre-trained":[98,119],"language":[99,120],"model":[100],"LLMs.":[102],"Specifically,":[103],"first":[105],"utilize":[106],"candidate":[108,159],"entity":[109,128,160],"pair":[110],"filtering":[113],"block,":[114],"based":[115],"model,":[121],"refine":[125],"all":[126,204],"possible":[127,142],"pairs":[129,161],"text,":[132],"ensuring":[133],"capture":[135],"as":[137,141,148,164,167],"much":[138],"information":[140],"Then,":[143],"fine-tuned":[145],"such":[147],"LLaMA":[149],"is":[150],"then":[151],"used":[152],"predict":[154],"relationship":[156],"possible.":[168],"Finally,":[169],"Filter":[171],"block":[172],"filter":[173],"extracted":[175],"LLMs,":[178],"remove":[180],"wrong":[182],"triples,":[183],"improve":[186],"precision":[188],"Experiments":[193],"several":[195],"public":[196],"datasets":[197],"show":[198],"that":[199],"CNRel":[200],"achieves":[201],"state-of-the-art":[202],"among":[203],"mainstream":[206],"relational":[207],"methods,":[210],"conduct":[213],"widely":[215],"ablation":[216],"experiments":[217],"reveal":[219],"contribution":[221],"each":[223],"component":[224],"overall":[227],"performance.":[228]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-29T00:00:00"}
