{"id":"https://openalex.org/W4399151092","doi":"https://doi.org/10.48550/arxiv.2405.16806","title":"Entity Alignment with Noisy Annotations from Large Language Models","display_name":"Entity Alignment with Noisy Annotations from Large Language Models","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4399151092","doi":"https://doi.org/10.48550/arxiv.2405.16806"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2405.16806","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.16806","pdf_url":"https://arxiv.org/pdf/2405.16806","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2405.16806","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004805571","display_name":"Shengyuan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chen, Shengyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058417704","display_name":"Qinggang Zhang","orcid":"https://orcid.org/0000-0002-1536-6529"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qinggang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038326486","display_name":"Junnan Dong","orcid":"https://orcid.org/0000-0003-2117-6083"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Junnan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004652254","display_name":"Wen Hua","orcid":"https://orcid.org/0000-0001-5456-7035"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hua, Wen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404176","display_name":"Qing Li","orcid":"https://orcid.org/0000-0003-3370-471X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100565926","display_name":"Xiao Huang","orcid":"https://orcid.org/0009-0006-1397-6821"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Xiao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5004805571"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9966999888420105,"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":0.9966999888420105,"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/T11719","display_name":"Data Quality and Management","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9919999837875366,"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":0.6980541348457336},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5985317230224609},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45091065764427185},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.341963529586792},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.0674387514591217}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6980541348457336},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5985317230224609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45091065764427185},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.341963529586792},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0674387514591217}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2405.16806","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.16806","pdf_url":"https://arxiv.org/pdf/2405.16806","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2405.16806","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.16806","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2405.16806","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.16806","pdf_url":"https://arxiv.org/pdf/2405.16806","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4399151092.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Entity":[0],"alignment":[1],"(EA)":[2],"aims":[3],"to":[4,27,56,64,99,113,140],"merge":[5],"two":[6],"knowledge":[7],"graphs":[8],"(KGs)":[9],"by":[10,52,119],"identifying":[11],"equivalent":[12],"entity":[13],"pairs.":[14],"While":[15],"existing":[16],"methods":[17],"heavily":[18],"rely":[19],"on":[20,126,155,170],"human-generated":[21],"labels,":[22],"it":[23,61],"is":[24,62,77],"prohibitively":[25],"expensive":[26],"incorporate":[28],"cross-domain":[29],"experts":[30],"for":[31,46,68,103],"annotation":[32,72,117],"in":[33,74,174],"real-world":[34,75],"scenarios.":[35],"The":[36],"advent":[37],"of":[38,168,176],"Large":[39],"Language":[40],"Models":[41],"(LLMs)":[42],"presents":[43],"new":[44],"avenues":[45],"automating":[47],"EA":[48,69,161],"with":[49],"annotations,":[50],"inspired":[51],"their":[53],"comprehensive":[54],"capability":[55],"process":[57],"semantic":[58],"information.":[59],"However,":[60],"nontrivial":[63],"directly":[65],"apply":[66],"LLMs":[67,79,102],"since":[70],"the":[71,88,116,121,127,152,156,166],"space":[73,118],"KGs":[76],"large.":[78],"could":[80],"also":[81],"generate":[82],"noisy":[83],"labels":[84],"that":[85],"may":[86],"mislead":[87],"alignment.":[89],"To":[90],"this":[91],"end,":[92],"we":[93,106,134],"propose":[94],"a":[95,108,159],"unified":[96],"framework,":[97],"LLM4EA,":[98],"effectively":[100],"leverage":[101],"EA.":[104],"Specifically,":[105],"design":[107],"novel":[109],"active":[110],"learning":[111],"policy":[112,153],"significantly":[114],"reduce":[115],"prioritizing":[120],"most":[122],"valuable":[123],"entities":[124],"based":[125,154],"entire":[128],"inter-KG":[129],"and":[130,179],"intra-KG":[131],"structure.":[132],"Moreover,":[133],"introduce":[135],"an":[136],"unsupervised":[137],"label":[138,143],"refiner":[139],"continuously":[141],"enhance":[142],"accuracy":[144],"through":[145],"in-depth":[146],"probabilistic":[147],"reasoning.":[148],"We":[149],"iteratively":[150],"optimize":[151],"feedback":[157],"from":[158],"base":[160],"model.":[162],"Extensive":[163],"experiments":[164],"demonstrate":[165],"advantages":[167],"LLM4EA":[169],"four":[171],"benchmark":[172],"datasets":[173],"terms":[175],"effectiveness,":[177],"robustness,":[178],"efficiency.":[180],"Codes":[181],"are":[182],"available":[183],"via":[184],"https://github.com/chensyCN/llm4ea_official.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}