{"id":"https://openalex.org/W7155979346","doi":"https://doi.org/10.1145/3774904.3792252","title":"Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion","display_name":"Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7155979346","doi":"https://doi.org/10.1145/3774904.3792252"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792252","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792252","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792252","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080665569","display_name":"Jiawei Sheng","orcid":"https://orcid.org/0000-0002-4865-982X"},"institutions":[{"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":"Jiawei Sheng","raw_affiliation_strings":["Institute of Information Engineering, CAS, Beijing, China, China"],"raw_orcid":"https://orcid.org/0000-0002-4865-982X","affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, CAS, Beijing, China, China","institution_ids":["https://openalex.org/I4210156404"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059851244","display_name":"Taoyu Su","orcid":"https://orcid.org/0009-0003-1674-7635"},"institutions":[{"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":"Taoyu Su","raw_affiliation_strings":["Institute of Information Engineering, CAS, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-1674-7635","affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210156404"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101641900","display_name":"Xixun Lin","orcid":"https://orcid.org/0009-0004-6645-0597"},"institutions":[{"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":"Xixun Lin","raw_affiliation_strings":["Institute of Information Engineering, CAS, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-6645-0597","affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210156404"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041607266","display_name":"Li X","orcid":"https://orcid.org/0000-0002-5981-2762"},"institutions":[{"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":"Xiaodong Li","raw_affiliation_strings":["Institute of Information Engineering, CAS, Beijing, China and School of Cyber Security, UCAS, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-7374-5413","affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, CAS, Beijing, China and School of Cyber Security, UCAS, Beijing, China","institution_ids":["https://openalex.org/I4210156404"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080686378","display_name":"Tingwen Liu","orcid":"https://orcid.org/0000-0002-0750-6923"},"institutions":[{"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":"Tingwen Liu","raw_affiliation_strings":["Institute of Information Engineering, CAS, Beijing, China and School of Cyber Security, UCAS, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0750-6923","affiliations":[{"raw_affiliation_string":"Institute of Information Engineering, CAS, Beijing, China and School of Cyber Security, UCAS, Beijing, China","institution_ids":["https://openalex.org/I4210156404"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210156404"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41417594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3744","last_page":"3754"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.858299970626831,"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":0.858299970626831,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.11129999905824661,"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":0.010700000450015068,"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/leverage","display_name":"Leverage (statistics)","score":0.6924999952316284},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.4668000042438507},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.46389999985694885},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.46059998869895935},{"id":"https://openalex.org/keywords/knowledge-transfer","display_name":"Knowledge transfer","score":0.39320001006126404},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39309999346733093},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.3783000111579895},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.3646000027656555},{"id":"https://openalex.org/keywords/conditional-independence","display_name":"Conditional independence","score":0.3440999984741211}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6924999952316284},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6464999914169312},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4668000042438507},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46560001373291016},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.46389999985694885},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.46059998869895935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45019999146461487},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3788999915122986},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36480000615119934},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3262999951839447},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.2597000002861023},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.2540999948978424}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3774904.3792252","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792252","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2607.03154","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2607.03154","pdf_url":"https://arxiv.org/pdf/2607.03154","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792252","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792252","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5441118188","display_name":null,"funder_award_id":"62406319","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W2499696929","https://openalex.org/W2551361256","https://openalex.org/W2728059831","https://openalex.org/W2759136286","https://openalex.org/W2808284704","https://openalex.org/W2950393809","https://openalex.org/W2963085847","https://openalex.org/W2964855489","https://openalex.org/W3012715399","https://openalex.org/W3100606581","https://openalex.org/W3111851097","https://openalex.org/W3151929433","https://openalex.org/W4224878657","https://openalex.org/W4288099666","https://openalex.org/W4312933868","https://openalex.org/W4361271817","https://openalex.org/W4367047153","https://openalex.org/W4385570557","https://openalex.org/W4385763801","https://openalex.org/W4386057777","https://openalex.org/W4389519012","https://openalex.org/W4390692489","https://openalex.org/W4392384521","https://openalex.org/W4393147873","https://openalex.org/W4396757568","https://openalex.org/W4398794965","https://openalex.org/W4400644945","https://openalex.org/W4401863536","https://openalex.org/W4403124784","https://openalex.org/W4403791927","https://openalex.org/W4408353548","https://openalex.org/W4410089548","https://openalex.org/W4410636953","https://openalex.org/W4416075211","https://openalex.org/W4417244077","https://openalex.org/W7126412293","https://openalex.org/W7133215870","https://openalex.org/W7138092928"],"related_works":[],"abstract_inverted_index":{"Multi-domain":[0],"knowledge":[1,17,80],"graph":[2],"completion":[3],"(MKGC)":[4],"aims":[5],"to":[6,33,87,161,175],"improve":[7],"missing":[8],"triple":[9],"prediction":[10,201],"in":[11,60,189,198,208],"a":[12,69,77,92,193],"target":[13],"KG":[14,54,90],"by":[15],"transferring":[16],"from":[18,52,136],"other":[19],"support":[20,111,137],"KGs.":[21,112,130,167],"Existing":[22],"methods":[23],"typically":[24],"enforce":[25],"consistency":[26],"constraints":[27],"on":[28,110,186],"equivalent":[29,134],"entities":[30,135],"across":[31,178],"KGs":[32,138,188],"transfer":[34,81],"knowledge,":[35],"which":[36,156],"risks":[37],"suppressing":[38],"domain-specific":[39,182],"contextual":[40],"information":[41,51],"of":[42,95],"entities.":[43],"This":[44],"design":[45],"can":[46],"also":[47,170],"compromise":[48],"entity":[49,97,104,118,122,149,200],"representation":[50],"all":[53],"domains,":[55],"impeding":[56],"performance":[57],"improvements,":[58],"especially":[59],"low-resource":[61,209],"data":[62,210],"scenarios.":[63],"To":[64],"address":[65],"this,":[66],"we":[67,114,132,169],"pioneer":[68],"generation-based":[70],"paradigm":[71],"for":[72],"MKGC":[73],"and":[74,100,124],"propose":[75],"DMKGC,":[76],"conditional":[78,141,159],"diffusion-guided":[79],"framework.":[82],"Our":[83],"key":[84],"insight":[85],"is":[86],"treat":[88],"each":[89],"as":[91,120,139,151],"partial":[93],"view":[94],"the":[96,140,147,152,172],"entire":[98],"information,":[99],"generate":[101],"informative":[102],"domain-general":[103],"embeddings":[105,119,150,174],"through":[106],"diffusion":[107,142],"models":[108],"conditioned":[109,166],"Particularly,":[113],"first":[115],"initialize":[116],"domain-agnostic":[117],"prior":[121,148],"embeddings,":[123],"then":[125],"encode":[126],"them":[127],"within":[128],"individual":[129],"Afterward,":[131],"fuse":[133],"generation":[143,154,160],"guidance.":[144],"We":[145],"leverage":[146],"proxy":[153],"objective,":[155],"ensures":[157],"this":[158],"be":[162,176],"unbiased":[163],"towards":[164],"any":[165],"Simultaneously,":[168],"train":[171],"generated":[173],"predictive":[177],"KGs,":[179],"thus":[180],"preserving":[181],"information.":[183],"Extensive":[184],"experiments":[185],"14":[187],"3":[190],"benchmarks":[191],"demonstrate":[192],"4.3%":[194],"average":[195],"MRR":[196],"improvement":[197],"tail":[199],"over":[202],"state-of-the-art":[203],"methods,":[204],"with":[205],"sustained":[206],"gains":[207],"settings.":[211]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2026-04-28T00:00:00"}