{"id":"https://openalex.org/W7156314501","doi":"https://doi.org/10.1145/3774904.3792481","title":"Relation-Aware Multimodal Analogical Reasoning with Modality Fingerprints and Adaptive Gating","display_name":"Relation-Aware Multimodal Analogical Reasoning with Modality Fingerprints and Adaptive Gating","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7156314501","doi":"https://doi.org/10.1145/3774904.3792481"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792481","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792481","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":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792481","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100600016","display_name":"Ruofan Wang","orcid":"https://orcid.org/0009-0000-8236-1122"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Ruofan Wang","raw_affiliation_strings":["The University of Auckland, Auckland City, New Zealand"],"raw_orcid":"https://orcid.org/0000-0003-4774-8633","affiliations":[{"raw_affiliation_string":"The University of Auckland, Auckland City, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079175213","display_name":"Zijian Huang","orcid":"https://orcid.org/0000-0002-0789-7389"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Zijian Huang","raw_affiliation_strings":["The University of Auckland, Auckland City, New Zealand"],"raw_orcid":"https://orcid.org/0000-0002-0789-7389","affiliations":[{"raw_affiliation_string":"The University of Auckland, Auckland City, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101519070","display_name":"Qiqi Wang","orcid":"https://orcid.org/0000-0002-7181-148X"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiqi Wang","raw_affiliation_strings":["Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-7181-148X","affiliations":[{"raw_affiliation_string":"Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110905959","display_name":"Yuchen Su","orcid":"https://orcid.org/0009-0007-5287-4163"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Yuchen Su","raw_affiliation_strings":["The University of Auckland, Auckland City, New Zealand"],"raw_orcid":"https://orcid.org/0009-0007-5287-4163","affiliations":[{"raw_affiliation_string":"The University of Auckland, Auckland City, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052941224","display_name":"Robert Amor","orcid":"https://orcid.org/0000-0002-4329-9044"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Robert Amor","raw_affiliation_strings":["The University of Auckland, Auckland City, New Zealand"],"raw_orcid":"https://orcid.org/0000-0002-4329-9044","affiliations":[{"raw_affiliation_string":"The University of Auckland, Auckland City, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134726027","display_name":"Kaiqi Zhao","orcid":"https://orcid.org/0000-0002-0984-1629"},"institutions":[{"id":"https://openalex.org/I158809036","display_name":"Shenzhen Institute of Information Technology","ror":"https://ror.org/03wrf9427","country_code":"CN","type":"education","lineage":["https://openalex.org/I158809036"]},{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiqi Zhao","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen, Shenzhen City, China"],"raw_orcid":"https://orcid.org/0000-0002-0984-1629","affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen, Shenzhen City, China","institution_ids":["https://openalex.org/I158809036","https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134695448","display_name":"Meng-Fen Chiang","orcid":"https://orcid.org/0009-0008-8385-0380"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Meng-Fen Chiang","raw_affiliation_strings":["National Yang Ming Chiao Tung University, Hsinchu City, Taiwan"],"raw_orcid":"https://orcid.org/0009-0008-8385-0380","affiliations":[{"raw_affiliation_string":"National Yang Ming Chiao Tung University, Hsinchu City, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4081","last_page":"4092"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.46720001101493835,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.46720001101493835,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.46059998869895935,"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":0.029500000178813934,"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/modal","display_name":"Modal","score":0.4862000048160553},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.47040000557899475},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.4523000121116638},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.43880000710487366},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4253999888896942},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.3961000144481659},{"id":"https://openalex.org/keywords/scene-graph","display_name":"Scene graph","score":0.3750999867916107}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6328999996185303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5449000000953674},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.4862000048160553},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.47040000557899475},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.4523000121116638},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4253999888896942},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C179372163","wikidata":"https://www.wikidata.org/wiki/Q1406181","display_name":"Scene graph","level":3,"score":0.3750999867916107},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3456000089645386},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34220001101493835},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792481","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792481","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"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792481","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792481","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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1550710032","https://openalex.org/W2127398084","https://openalex.org/W2250539671","https://openalex.org/W2460442863","https://openalex.org/W2526174222","https://openalex.org/W2561529111","https://openalex.org/W2908230750","https://openalex.org/W2945623882","https://openalex.org/W2963690694","https://openalex.org/W2979129662","https://openalex.org/W3177379566","https://openalex.org/W3207311065","https://openalex.org/W4229024390","https://openalex.org/W4245954906","https://openalex.org/W4285255684","https://openalex.org/W4312910992","https://openalex.org/W4387967963","https://openalex.org/W4388776291","https://openalex.org/W4405018244"],"related_works":[],"abstract_inverted_index":{"Analogical":[0,83],"reasoning":[1],"over":[2],"Multimodal":[3,82],"Knowledge":[4],"Graphs":[5],"(MMKGs)":[6],"couples":[7],"abductive":[8],"relation":[9,100],"induction":[10,101],"with":[11,86,150,160],"inductive":[12],"tail":[13,106],"completion.":[14,107],"However,":[15],"existing":[16],"approaches":[17],"rely":[18],"on":[19,122,189],"static":[20],"fusion":[21,104],"mechanisms":[22],"that":[23,173,194],"overlook":[24],"the":[25,65,70,123,129],"inherent":[26],"asymmetry":[27],"of":[28,131],"modal":[29,202],"relevance:":[30],"while":[31],"visual":[32,119],"cues":[33],"elucidate":[34],"concrete":[35,136],"entities,":[36,137],"they":[37],"are":[38],"often":[39],"noisy":[40],"or":[41,206],"irrelevant":[42],"for":[43],"abstract":[44,151],"concepts,":[45],"where":[46],"text":[47],"and":[48,64,102,118,141,153,158,166,181],"graph":[49,163],"structure":[50],"provide":[51],"decisive":[52],"signals.":[53],"Furthermore,":[54],"prior":[55],"methods":[56],"fail":[57],"to":[58,96,114],"enforce":[59],"consistency":[60],"between":[61],"induced":[62],"relations":[63],"modality":[66,94],"patterns":[67],"implied":[68],"by":[69],"analogical":[71],"context.":[72,126],"To":[73,127],"bridge":[74],"this":[75],"gap,":[76],"we":[77,138],"introduce":[78],"RMAR,":[79],"a":[80,146,185],"Relation-aware":[81],"Reasoning":[84],"framework":[85],"two":[87],"complementary":[88],"paths.":[89],"An":[90,108],"explicit":[91],"path":[92,110],"estimates":[93],"fingerprints":[95],"score":[97],"compatibility":[98],"during":[99,105],"guide":[103],"implicit":[109],"employs":[111],"adaptive":[112],"gating":[113],"blend":[115],"structural,":[116],"textual,":[117],"signals":[120],"conditioned":[121],"specific":[124],"query":[125],"address":[128],"limitations":[130],"current":[132],"benchmarks,":[133],"which":[134],"overrepresent":[135],"release":[139],"MCNetAnalogy,":[140],"its":[142],"companion":[143],"graph,":[144],"MCNetKG,":[145],"rigorous":[147],"dataset":[148],"enriched":[149],"concepts":[152],"actions.":[154],"RMAR":[155,174],"is":[156,198,204],"backbone-agnostic":[157],"works":[159],"multimodal":[161],"knowledge":[162],"embedding":[164],"(MKGE)":[165],"transformer-based":[167,182],"(MPT)":[168],"pipelines.":[169],"Extensive":[170],"experiments":[171],"demonstrate":[172],"delivers":[175],"consistent":[176],"gains":[177],"across":[178],"both":[179],"embedding-based":[180],"backbones,":[183],"achieving":[184],"29%":[186],"relative":[187],"improvement":[188],"MCNetAnalogy.":[190],"Ablation":[191],"studies":[192],"confirm":[193],"RMAR's":[195],"relation-aware":[196],"modulation":[197],"particularly":[199],"effective":[200],"when":[201],"evidence":[203],"weak":[205],"ambiguous.":[207]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-04-28T00:00:00"}