{"id":"https://openalex.org/W4312235940","doi":"https://doi.org/10.1109/icpr56361.2022.9956509","title":"ComDensE : Combined Dense Embedding of Relation-aware and Common Features for Knowledge Graph Completion","display_name":"ComDensE : Combined Dense Embedding of Relation-aware and Common Features for Knowledge Graph Completion","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312235940","doi":"https://doi.org/10.1109/icpr56361.2022.9956509"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956509","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956509","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070267996","display_name":"Minsang Kim","orcid":"https://orcid.org/0009-0008-4016-4735"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Minsang Kim","raw_affiliation_strings":["Kakao Enterprise"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kakao Enterprise","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110957263","display_name":"Seungjun Baek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seungjun Baek","raw_affiliation_strings":["Korea University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5191,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.64431487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"26","issue":null,"first_page":"1989","last_page":"1995"},"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":0.9926000237464905,"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":0.9810000061988831,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7493224740028381},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6884289383888245},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6570438742637634},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.61024409532547},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.5734617114067078},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5629715919494629},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5172343850135803},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5170627236366272},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48441746830940247},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4802696704864502},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4771522879600525},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4590022563934326},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42705458402633667},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4199541211128235},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4112069308757782},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3772379457950592},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36091217398643494},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36012744903564453}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7493224740028381},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6884289383888245},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6570438742637634},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.61024409532547},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.5734617114067078},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5629715919494629},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5172343850135803},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5170627236366272},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48441746830940247},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4802696704864502},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4771522879600525},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4590022563934326},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42705458402633667},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4199541211128235},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4112069308757782},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3772379457950592},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36091217398643494},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36012744903564453},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956509","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956509","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1533230146","https://openalex.org/W1552847225","https://openalex.org/W2016753842","https://openalex.org/W2081580037","https://openalex.org/W2094728533","https://openalex.org/W2107598941","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2250184916","https://openalex.org/W2250342289","https://openalex.org/W2283196293","https://openalex.org/W2432356473","https://openalex.org/W2521367263","https://openalex.org/W2604314403","https://openalex.org/W2728059831","https://openalex.org/W2949434543","https://openalex.org/W2949972983","https://openalex.org/W2950393809","https://openalex.org/W2964015378","https://openalex.org/W2997837621","https://openalex.org/W3005977214","https://openalex.org/W3035707368","https://openalex.org/W3040558716","https://openalex.org/W3091432621","https://openalex.org/W3104415840","https://openalex.org/W3124660976","https://openalex.org/W3167967639","https://openalex.org/W3171434230","https://openalex.org/W3176750236","https://openalex.org/W6631190155","https://openalex.org/W6631964550","https://openalex.org/W6678830454","https://openalex.org/W6678846912","https://openalex.org/W6718112784","https://openalex.org/W6726873649","https://openalex.org/W6758075616","https://openalex.org/W6771485939","https://openalex.org/W6780022207","https://openalex.org/W6783268361","https://openalex.org/W6783806699","https://openalex.org/W6785858038","https://openalex.org/W6791866938","https://openalex.org/W6796595205"],"related_works":["https://openalex.org/W2604454537","https://openalex.org/W4206028705","https://openalex.org/W2808284704","https://openalex.org/W2883748392","https://openalex.org/W2897702399","https://openalex.org/W2757431232","https://openalex.org/W2954554213","https://openalex.org/W3200431764","https://openalex.org/W4288286922","https://openalex.org/W4206547516"],"abstract_inverted_index":{"Real-world":[0],"knowledge":[1],"graphs":[2],"(KG)":[3,25],"are":[4,115],"mostly":[5],"incomplete.":[6],"The":[7],"problem":[8],"of":[9,30,133,158,166],"recovering":[10],"missing":[11],"relations,":[12,33],"called":[13],"KG":[14,39],"completion,":[15],"has":[16],"recently":[17],"become":[18],"an":[19,90,150],"active":[20],"research":[21],"area.":[22],"Knowledge":[23],"graph":[24],"embedding,":[26],"a":[27,59],"low-dimensional":[28],"representation":[29],"entities":[31],"and":[32,51,63,69,138,162],"is":[34],"the":[35,77,98,104,124,128,144,156,159,163,167,173,182],"crucial":[36],"technique":[37],"for":[38],"completion.":[40],"Convolutional":[41],"neural":[42,74],"networks":[43],"in":[44,120,127,131,179],"models":[45],"such":[46],"as":[47,177],"ConvE,":[48],"SACN,":[49],"InteractE,":[50],"RGCN":[52],"achieve":[53],"recent":[54],"successes.":[55],"This":[56],"paper":[57],"takes":[58],"different":[60],"architectural":[61],"view":[62],"proposes":[64],"ComDensE":[65,122,180],"which":[66],"combines":[67],"relation-aware":[68,78,160],"common":[70,99,105,164],"features":[71],"using":[72,117],"dense":[73,118,175],"networks.":[75],"In":[76,97],"feature":[79,100],"extraction,":[80,101],"we":[81,102],"attempt":[82],"to":[83,94,108,143,154],"create":[84],"relational":[85],"inductive":[86],"bias":[87],"by":[88],"applying":[89],"encoding":[91,106,113],"function":[92,107],"specific":[93],"each":[95],"relation.":[96],"apply":[103],"all":[109],"input":[110],"embeddings.":[111],"These":[112],"functions":[114],"implemented":[116,178],"layers":[119],"ComDensE.":[121,168],"achieves":[123,181],"state-of-the-art":[125],"performance":[126],"link":[129],"prediction":[130],"terms":[132],"MRR,":[134],"HIT@1":[135,139],"on":[136,140],"FB15k-237":[137],"WN18RR":[141],"compared":[142],"previous":[145],"baseline":[146],"approaches.":[147],"We":[148],"conduct":[149],"extensive":[151],"ablation":[152],"study":[153],"examine":[155],"effects":[157],"layer":[161,165],"Experimental":[169],"results":[170],"illustrate":[171],"that":[172],"combined":[174],"architecture":[176],"best":[183],"performance.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
