{"id":"https://openalex.org/W4413958901","doi":"https://doi.org/10.3390/e27090924","title":"Learnable Convolutional Attention Network for Unsupervised Knowledge Graph Entity Alignment","display_name":"Learnable Convolutional Attention Network for Unsupervised Knowledge Graph Entity Alignment","publication_year":2025,"publication_date":"2025-09-03","ids":{"openalex":"https://openalex.org/W4413958901","doi":"https://doi.org/10.3390/e27090924","pmid":"https://pubmed.ncbi.nlm.nih.gov/41008050"},"language":"en","primary_location":{"id":"doi:10.3390/e27090924","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27090924","pdf_url":"https://www.mdpi.com/1099-4300/27/9/924/pdf?version=1756870718","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/27/9/924/pdf?version=1756870718","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029010934","display_name":"Weishan Cai","orcid":"https://orcid.org/0000-0003-2416-6841"},"institutions":[{"id":"https://openalex.org/I32246829","display_name":"Guangdong University of Education","ror":"https://ror.org/0574der91","country_code":"CN","type":"education","lineage":["https://openalex.org/I32246829"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weishan Cai","raw_affiliation_strings":["School of Computer Science, Guangdong University of Education, Guangzhou 510303, China"],"raw_orcid":"https://orcid.org/0000-0003-2416-6841","affiliations":[{"raw_affiliation_string":"School of Computer Science, Guangdong University of Education, Guangzhou 510303, China","institution_ids":["https://openalex.org/I32246829"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114444391","display_name":"Wenjun Ma","orcid":"https://orcid.org/0009-0003-3829-8383"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenjun Ma","raw_affiliation_strings":["Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Foshan 528225, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Foshan 528225, China","institution_ids":["https://openalex.org/I187400657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5114444391"],"corresponding_institution_ids":["https://openalex.org/I187400657"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":1.6643,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87896989,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"27","issue":"9","first_page":"924","last_page":"924"},"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":0.9991999864578247,"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/T10028","display_name":"Topic Modeling","score":0.9969000220298767,"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":0.6345455050468445},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.546345591545105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4915243089199066},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4351505637168884},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.41277122497558594},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37586307525634766},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2956005334854126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6345455050468445},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.546345591545105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4915243089199066},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4351505637168884},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.41277122497558594},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37586307525634766},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2956005334854126}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e27090924","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27090924","pdf_url":"https://www.mdpi.com/1099-4300/27/9/924/pdf?version=1756870718","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:41008050","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41008050","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:17bee89279744653b87dc6fc4620aee4","is_oa":true,"landing_page_url":"https://doaj.org/article/17bee89279744653b87dc6fc4620aee4","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":"Entropy, Vol 27, Iss 9, p 924 (2025)","raw_type":"article"},{"id":"pmh:oai:europepmc.org:11276565","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12468323","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"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":"pmh:oai:pubmedcentral.nih.gov:12468323","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12468323/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"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":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e27090924","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27090924","pdf_url":"https://www.mdpi.com/1099-4300/27/9/924/pdf?version=1756870718","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5411578869","display_name":null,"funder_award_id":"2023GXJK422","funder_id":"https://openalex.org/F3057939197","funder_display_name":"Comanche Nation"},{"id":"https://openalex.org/G7656413971","display_name":null,"funder_award_id":"62306079","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F3057939197","display_name":"Comanche Nation","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413958901.pdf","grobid_xml":"https://content.openalex.org/works/W4413958901.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W2551361256","https://openalex.org/W2808284704","https://openalex.org/W2890187992","https://openalex.org/W2903963001","https://openalex.org/W2955238243","https://openalex.org/W2962916648","https://openalex.org/W2964855489","https://openalex.org/W2966055354","https://openalex.org/W2971001654","https://openalex.org/W2997062749","https://openalex.org/W3001896264","https://openalex.org/W3039695075","https://openalex.org/W3089874281","https://openalex.org/W3098038527","https://openalex.org/W3098583774","https://openalex.org/W3146701396","https://openalex.org/W3156859417","https://openalex.org/W3198296722","https://openalex.org/W3210247849","https://openalex.org/W3211203754","https://openalex.org/W4224309032","https://openalex.org/W4224310474","https://openalex.org/W4226047653","https://openalex.org/W4226093342","https://openalex.org/W4281727300","https://openalex.org/W4289533781","https://openalex.org/W4290785452","https://openalex.org/W4292794811","https://openalex.org/W4306317022","https://openalex.org/W4310354946","https://openalex.org/W4313000383","https://openalex.org/W4321793983","https://openalex.org/W4362555417","https://openalex.org/W4366773981","https://openalex.org/W4385573032","https://openalex.org/W4385657310","https://openalex.org/W4386811781","https://openalex.org/W4389520134","https://openalex.org/W4391018571","https://openalex.org/W4409001376","https://openalex.org/W4410001068"],"related_works":["https://openalex.org/W4391621807","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391621790","https://openalex.org/W4239306820","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"The":[0],"success":[1],"of":[2,21,38,63,90,121,128,139,160,178,190,202],"current":[3],"entity":[4,105,180],"alignment":[5,211],"(EA)":[6],"tasks":[7],"largely":[8],"depends":[9],"on":[10,41,150,169,187],"the":[11,19,56,61,86,115,119,126,156,161,176,200,226,230],"supervision":[12],"information":[13,123,138],"provided":[14],"by":[15,60,221],"labeled":[16,22,64],"data.":[17,65],"Considering":[18],"cost":[20],"data,":[23],"most":[24],"supervised":[25,215],"methods":[26,70],"are":[27],"challenging":[28],"to":[29,54,84,132,173,198],"apply":[30],"in":[31,223,229],"practical":[32],"scenarios.":[33],"Therefore,":[34],"an":[35],"increasing":[36],"number":[37],"works":[39],"based":[40,149,168],"contrastive":[42],"learning,":[43,45],"active":[44],"or":[46,81,216],"other":[47],"deep":[48],"learning":[49],"techniques":[50],"have":[51],"been":[52],"developed,":[53],"solve":[55],"performance":[57],"bottleneck":[58],"caused":[59],"lack":[62],"However,":[66],"existing":[67],"unsupervised":[68,104,217],"EA":[69,162],"still":[71],"face":[72],"certain":[73],"limitations;":[74],"either":[75],"their":[76],"modeling":[77],"complexity":[78],"is":[79,171],"high":[80],"they":[82],"fail":[83],"balance":[85],"effectiveness":[87],"and":[88,124,158,193,196,219],"practicality":[89],"alignment.":[91],"To":[92],"overcome":[93],"these":[94],"issues,":[95],"we":[96,183],"propose":[97],"a":[98,144,165],"learnable":[99],"convolutional":[100],"attention":[101,116],"network":[102],"for":[103],"alignment,":[106],"named":[107],"LCA-UEA.":[108,203],"Specifically,":[109],"LCA-UEA":[110,142,208],"performs":[111],"convolution":[112],"operations":[113],"before":[114],"mechanism,":[117],"ensuring":[118],"acquisition":[120],"structural":[122],"avoiding":[125],"superposition":[127],"redundant":[129],"information.":[130],"Then,":[131],"efficiently":[133],"filter":[134],"out":[135],"invalid":[136],"neighborhood":[137],"aligned":[140],"entities,":[141],"designs":[143],"relation":[145],"structure":[146],"reconstruction":[147],"method":[148],"potential":[151],"matching":[152],"relations,":[153],"thereby":[154],"enhancing":[155],"usability":[157],"scalability":[159],"method.":[163],"Notably,":[164],"similarity":[166,177],"function":[167],"consistency":[170],"proposed":[172],"better":[174],"measure":[175],"candidate":[179],"pairs.":[181],"Finally,":[182],"conducted":[184],"extensive":[185],"experiments":[186],"three":[188],"datasets":[189],"different":[191],"sizes":[192],"types":[194],"(cross-lingual":[195],"monolingual)":[197],"verify":[199],"superiority":[201],"Experimental":[204],"results":[205],"demonstrate":[206],"that":[207],"significantly":[209],"improved":[210],"accuracy,":[212],"outperforming":[213],"25":[214],"methods,":[218],"improving":[220],"6.4%":[222],"Hits@1":[224],"over":[225],"best":[227,231],"baseline":[228],"case.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-24T13:16:06.693445","created_date":"2025-10-10T00:00:00"}