{"id":"https://openalex.org/W4399422997","doi":"https://doi.org/10.1145/3652583.3658085","title":"Multi-modal Entity Alignment via Position-enhanced Multi-label Propagation","display_name":"Multi-modal Entity Alignment via Position-enhanced Multi-label Propagation","publication_year":2024,"publication_date":"2024-05-30","ids":{"openalex":"https://openalex.org/W4399422997","doi":"https://doi.org/10.1145/3652583.3658085"},"language":"en","primary_location":{"id":"doi:10.1145/3652583.3658085","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658085","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658085","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658085","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036258889","display_name":"Wei Tang","orcid":"https://orcid.org/0000-0002-9250-4163"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Tang","raw_affiliation_strings":["Huawei Translation Services Center, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9250-4163","affiliations":[{"raw_affiliation_string":"Huawei Translation Services Center, Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066011878","display_name":"Yuanyi Wang","orcid":"https://orcid.org/0009-0009-8909-5774"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyi Wang","raw_affiliation_strings":["Huawei Test & ATE Dept, CRDU, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0009-8909-5774","affiliations":[{"raw_affiliation_string":"Huawei Test & ATE Dept, CRDU, Guangdong, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036258889"],"corresponding_institution_ids":["https://openalex.org/I2250955327"],"apc_list":null,"apc_paid":null,"fwci":0.3311,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62724819,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"366","last_page":"375"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T11273","display_name":"Advanced Graph Neural Networks","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/T10028","display_name":"Topic Modeling","score":0.9980999827384949,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.8363543152809143},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6277996897697449},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5635189414024353},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5325638055801392},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5162946581840515},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5144476890563965},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4777059555053711},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3614429235458374},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3342375159263611},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0847616195678711}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8363543152809143},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6277996897697449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5635189414024353},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5325638055801392},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5162946581840515},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5144476890563965},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4777059555053711},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3614429235458374},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3342375159263611},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0847616195678711},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3652583.3658085","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658085","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658085","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3652583.3658085","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658085","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658085","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4399422997.pdf","grobid_xml":"https://content.openalex.org/works/W4399422997.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W2057069782","https://openalex.org/W2133299088","https://openalex.org/W2154851992","https://openalex.org/W2526174222","https://openalex.org/W2788798739","https://openalex.org/W2808284704","https://openalex.org/W2911778742","https://openalex.org/W2949700412","https://openalex.org/W2963606508","https://openalex.org/W2963870853","https://openalex.org/W2964418074","https://openalex.org/W2966055354","https://openalex.org/W2970921796","https://openalex.org/W2997062749","https://openalex.org/W3089874281","https://openalex.org/W3091993229","https://openalex.org/W3092965216","https://openalex.org/W3093922720","https://openalex.org/W3104097132","https://openalex.org/W3125808187","https://openalex.org/W3192484213","https://openalex.org/W4229024390","https://openalex.org/W4239019441","https://openalex.org/W4284689965","https://openalex.org/W4290875677","https://openalex.org/W4290927860","https://openalex.org/W4293262063","https://openalex.org/W4304013709","https://openalex.org/W4304099316","https://openalex.org/W4321485574","https://openalex.org/W4367016889","https://openalex.org/W4384890951","https://openalex.org/W4385567259","https://openalex.org/W6600459194"],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W4235240664","https://openalex.org/W2965083567","https://openalex.org/W1838576100","https://openalex.org/W2095886385","https://openalex.org/W2889616422","https://openalex.org/W2020521331","https://openalex.org/W101162712","https://openalex.org/W4256618828","https://openalex.org/W2786691313"],"abstract_inverted_index":{"Multi-modal":[0],"Entity":[1],"Alignment":[2],"(MMEA)":[3],"refers":[4],"to":[5,15,23,85,96,132],"utilizing":[6],"multiple":[7,19],"modalities":[8],"such":[9,56],"as":[10,57],"text,":[11],"images,":[12],"videos,":[13],"etc.,":[14],"match":[16],"entities":[17,71],"from":[18],"knowledge":[20],"graphs.":[21],"Compared":[22],"single-modal":[24],"entity":[25,28,37,73,123,143],"alignment,":[26],"multi-modal":[27,68,122,137],"alignment":[29,74,124,150,170],"can":[30,62],"provide":[31],"a":[32,119],"more":[33],"comprehensive":[34],"description":[35],"of":[36,51,70,93,99,105,148,168],"semantics":[38],"and":[39,65,134,138,172],"improve":[40],"matching":[41],"accuracy.":[42],"Currently,":[43],"research":[44],"efforts":[45],"are":[46,146],"directed":[47],"towards":[48],"the":[49,67,90,97,103,128],"development":[50],"sophisticated":[52],"deep":[53],"learning":[54],"models,":[55],"graph":[58],"neural":[59],"networks,":[60],"that":[61,126,145,159],"effectively":[63],"capture":[64],"integrate":[66],"features":[69],"for":[72],"tasks.":[75],"While":[76],"these":[77,106,114],"models":[78,107],"have":[79],"shown":[80],"promising":[81],"results,":[82],"they":[83],"tend":[84],"focus":[86],"on":[87,154],"capturing":[88],"only":[89],"local":[91],"structure":[92],"entities,":[94],"leading":[95],"challenge":[98],"subgraph":[100],"isomorphism.":[101],"Moreover,":[102],"complexity":[104],"often":[108],"hinders":[109],"their":[110],"scalability.":[111],"To":[112],"address":[113],"limitations,":[115],"this":[116],"paper":[117],"proposes":[118],"non-neural,":[120],"position-enhanced":[121],"algorithm":[125],"leverages":[127],"label":[129],"propagation":[130],"technique":[131],"fuse":[133],"aggregate":[135],"various":[136,155],"position":[139],"features,":[140],"resulting":[141],"in":[142,166],"representations":[144],"aware":[147],"long-term":[149],"information.":[151],"Extensive":[152],"experiments":[153],"public":[156],"datasets":[157],"demonstrate":[158],"our":[160],"proposed":[161],"approach":[162],"outperforms":[163],"state-of-the-art":[164],"algorithms":[165],"terms":[167],"both":[169],"accuracy":[171],"computational":[173],"efficiency.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}