{"id":"https://openalex.org/W2808284704","doi":"https://doi.org/10.24963/ijcai.2018/611","title":"Bootstrapping Entity Alignment with Knowledge Graph Embedding","display_name":"Bootstrapping Entity Alignment with Knowledge Graph Embedding","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2808284704","doi":"https://doi.org/10.24963/ijcai.2018/611","mag":"2808284704"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2018/611","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/611","pdf_url":"https://www.ijcai.org/proceedings/2018/0611.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2018/0611.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101848528","display_name":"Zequn Sun","orcid":"https://orcid.org/0000-0003-4177-9199"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zequn Sun","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100727084","display_name":"Wei Hu","orcid":"https://orcid.org/0000-0003-3635-6335"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Hu","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062347892","display_name":"Qinghe Zhang","orcid":"https://orcid.org/0000-0002-7251-5105"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingheng Zhang","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089586983","display_name":"Yuzhong Qu","orcid":"https://orcid.org/0000-0003-2777-8149"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuzhong Qu","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":24.1076,"has_fulltext":false,"cited_by_count":541,"citation_normalized_percentile":{"value":0.99508279,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"4396","last_page":"4402"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9916999936103821,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9868999719619751,"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/bootstrapping","display_name":"Bootstrapping (finance)","score":0.9185302257537842},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.8973495960235596},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7999686002731323},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.7638636231422424},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5720729231834412},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5161126852035522},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5044571161270142},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.49009788036346436},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4412986636161804},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4214915931224823},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4021334648132324},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3777625560760498},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3636314272880554},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3500615358352661},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10910353064537048},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.07019135355949402}],"concepts":[{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.9185302257537842},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.8973495960235596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7999686002731323},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.7638636231422424},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5720729231834412},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5161126852035522},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5044571161270142},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.49009788036346436},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4412986636161804},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4214915931224823},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4021334648132324},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3777625560760498},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3636314272880554},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3500615358352661},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10910353064537048},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.07019135355949402},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2018/611","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/611","pdf_url":"https://www.ijcai.org/proceedings/2018/0611.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2018/611","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/611","pdf_url":"https://www.ijcai.org/proceedings/2018/0611.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322015","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2808284704.pdf","grobid_xml":"https://content.openalex.org/works/W2808284704.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W1426956448","https://openalex.org/W2101210369","https://openalex.org/W2118953734","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2146502635","https://openalex.org/W2152005244","https://openalex.org/W2157896838","https://openalex.org/W2184957013","https://openalex.org/W2226877337","https://openalex.org/W2283196293","https://openalex.org/W2405102607","https://openalex.org/W2551361256","https://openalex.org/W2577076988","https://openalex.org/W2725395424","https://openalex.org/W2728059831","https://openalex.org/W2741750617","https://openalex.org/W2761081518","https://openalex.org/W2768012150","https://openalex.org/W2951078529","https://openalex.org/W2951938755","https://openalex.org/W2962916648","https://openalex.org/W2963063567","https://openalex.org/W2964116313","https://openalex.org/W2964194917"],"related_works":["https://openalex.org/W2808284704","https://openalex.org/W2130553454","https://openalex.org/W3022007134","https://openalex.org/W4317548404","https://openalex.org/W2087783760","https://openalex.org/W1509924131","https://openalex.org/W2883748392","https://openalex.org/W3163689946","https://openalex.org/W4287890939","https://openalex.org/W2033364610"],"abstract_inverted_index":{"Embedding-based":[0],"entity":[1,13,20,54,60,100],"alignment":[2,14,39,61,74,110],"represents":[3],"different":[4],"knowledge":[5],"graphs":[6],"(KGs)":[7],"as":[8,40,62],"low-dimensional":[9],"embeddings":[10],"and":[11,109],"finds":[12],"by":[15,33],"measuring":[16],"the":[17,34,90,95,116],"similarities":[18],"between":[19],"embeddings.":[21,69],"Existing":[22],"approaches":[23],"have":[24],"achieved":[25],"promising":[26],"results,":[27],"however,":[28],"they":[29],"are":[30],"still":[31],"challenged":[32],"lack":[35],"of":[36],"enough":[37],"prior":[38],"labeled":[41],"training":[42,63],"data.":[43],"In":[44],"this":[45],"paper,":[46],"we":[47],"propose":[48],"a":[49],"bootstrapping":[50,107],"approach":[51,92],"to":[52,77,115],"embedding-based":[53,97],"alignment.":[55,101],"It":[56],"iteratively":[57],"labels":[58],"likely":[59],"data":[64],"for":[65,99],"learning":[66],"alignment-oriented":[67,104],"KG":[68,105],"Furthermore,":[70],"it":[71],"employs":[72],"an":[73],"editing":[75,111],"method":[76,112],"reduce":[78],"error":[79],"accumulation":[80],"during":[81],"iterations.":[82],"Our":[83],"experiments":[84],"on":[85],"real-world":[86],"datasets":[87],"showed":[88],"that":[89],"proposed":[91,103],"significantly":[93],"outperformed":[94],"state-of-the-art":[96],"ones":[98],"The":[102],"embedding,":[106],"process":[108],"all":[113],"contributed":[114],"performance":[117],"improvement.":[118]},"counts_by_year":[{"year":2026,"cited_by_count":26},{"year":2025,"cited_by_count":67},{"year":2024,"cited_by_count":69},{"year":2023,"cited_by_count":89},{"year":2022,"cited_by_count":77},{"year":2021,"cited_by_count":102},{"year":2020,"cited_by_count":73},{"year":2019,"cited_by_count":35},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
