{"id":"https://openalex.org/W4403582440","doi":"https://doi.org/10.1145/3627673.3679860","title":"Transformer Based Bayesian Network Embedding for Efficient Multiple Probabilistic Inferences","display_name":"Transformer Based Bayesian Network Embedding for Efficient Multiple Probabilistic Inferences","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403582440","doi":"https://doi.org/10.1145/3627673.3679860"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679860","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679860","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3679860","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","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/10.1145/3627673.3679860","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041269918","display_name":"Kun Yue","orcid":"https://orcid.org/0000-0003-3641-1461"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kun Yue","raw_affiliation_strings":["School of Information Science and Engineering & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080755859","display_name":"Zhiwei Qi","orcid":"https://orcid.org/0000-0002-5968-8503"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwei Qi","raw_affiliation_strings":["School of Education & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China"],"affiliations":[{"raw_affiliation_string":"School of Education & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101763318","display_name":"Liang Duan","orcid":"https://orcid.org/0000-0001-9473-2533"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Duan","raw_affiliation_strings":["School of Information Science and Engineering & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering & Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, China","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5041269918"],"corresponding_institution_ids":["https://openalex.org/I189210763"],"apc_list":null,"apc_paid":null,"fwci":0.3393,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67263599,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3073","last_page":"3082"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","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/T11303","display_name":"Bayesian Modeling and Causal Inference","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":0.9800999760627747,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9667999744415283,"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/bayesian-network","display_name":"Bayesian network","score":0.6872752904891968},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6727160215377808},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6726874709129333},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5552487373352051},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5138385891914368},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.46829548478126526},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4248572587966919},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34087827801704407},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3388708531856537},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11604100465774536},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.054980188608169556}],"concepts":[{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.6872752904891968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6727160215377808},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6726874709129333},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5552487373352051},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5138385891914368},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.46829548478126526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4248572587966919},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34087827801704407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3388708531856537},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11604100465774536},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.054980188608169556},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3679860","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679860","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3679860","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3627673.3679860","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679860","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3679860","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G160098265","display_name":null,"funder_award_id":"202401","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8280367463","display_name":null,"funder_award_id":"A2029","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8977137560","display_name":null,"funder_award_id":"U23A2029","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":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403582440.pdf","grobid_xml":"https://content.openalex.org/works/W4403582440.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W1984123657","https://openalex.org/W2101988652","https://openalex.org/W2143075689","https://openalex.org/W2466989778","https://openalex.org/W2602856279","https://openalex.org/W2610309919","https://openalex.org/W2766800087","https://openalex.org/W3028571922","https://openalex.org/W3104002127","https://openalex.org/W3164354101","https://openalex.org/W3187966659","https://openalex.org/W4200494586","https://openalex.org/W4283452349","https://openalex.org/W4318541522","https://openalex.org/W4319068889","https://openalex.org/W4367047497","https://openalex.org/W4380853321","https://openalex.org/W4382239927","https://openalex.org/W4385570922","https://openalex.org/W4385767706","https://openalex.org/W4386075553","https://openalex.org/W4387848566","https://openalex.org/W4393190187"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2037549926","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W2943623134","https://openalex.org/W3006008237","https://openalex.org/W2494523064","https://openalex.org/W2419146053","https://openalex.org/W1734881440"],"abstract_inverted_index":{"Bayesian":[0],"network":[1],"(BN)":[2],"is":[3,169],"a":[4],"directed":[5],"acyclic":[6],"graph":[7],"(DAG)":[8],"representing":[9],"the":[10,38,49,70,87,94,99,108,122,126,131,136,173,184],"dependence":[11],"relations":[12],"among":[13],"random":[14],"variables":[15],"with":[16],"conditional":[17],"probability":[18],"tables":[19],"(CPTs).":[20],"The":[21],"efficiency":[22],"and":[23,57,76,89,102,106,157],"accuracy":[24],"of":[25,37,51,72,111,117,130,138,179],"multiple":[26,80,148],"probabilistic":[27,60,123,149],"inferences":[28,150],"in":[29,133,160],"BN":[30,54,78,134],"could":[31],"not":[32],"be":[33],"guaranteed":[34],"by":[35,97,151,176],"most":[36],"existing":[39],"approximate":[40],"inference":[41,124,167],"methods.":[42],"To":[43],"address":[44],"this":[45],"issue,":[46],"we":[47,63,92,120,142],"propose":[48],"methods":[50],"Transformer":[52,95,112],"based":[53,59],"embedding":[55,153],"(TBNE)":[56],"TBNE":[58],"inferences.":[61],"Specifically,":[62],"first":[64],"adopt":[65],"mutual":[66],"information":[67,128,139],"to":[68,113],"measure":[69],"weight":[71],"parent-child":[73],"node":[74,100,115],"pairs":[75],"transform":[77],"into":[79],"bidirectional":[81],"weighted":[82],"graphs":[83],"(BWGs),":[84],"while":[85,181],"preserving":[86],"DAG":[88],"CPTs.":[90],"Then,":[91],"redesign":[93],"model":[96],"incorporating":[98],"importance":[101],"shortest":[103],"path":[104,132],"encodings,":[105],"extend":[107],"self-attention":[109],"module":[110],"generate":[114],"embeddings":[116],"BWGs.":[118],"Following,":[119],"cast":[121],"as":[125],"decoding":[127],"maximization":[129],"from":[135],"perspective":[137],"theory.":[140],"Finally,":[141],"give":[143],"an":[144],"efficient":[145,171],"algorithm":[146],"for":[147],"calculating":[152],"similarities":[154],"between":[155],"evidence":[156],"query":[158],"nodes":[159],"BN.":[161],"Experimental":[162],"results":[163],"show":[164],"that":[165],"our":[166],"method":[168],"more":[170],"than":[172],"state-of-the-art":[174],"competitors":[175],"several":[177],"orders":[178],"magnitude":[180],"maintaining":[182],"almost":[183],"same":[185],"results.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}