{"id":"https://openalex.org/W4402352964","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650666","title":"A Transformer-based Knowledge Graph Embedding Model Combining Graph Paths and Local Neighborhood","display_name":"A Transformer-based Knowledge Graph Embedding Model Combining Graph Paths and Local Neighborhood","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352964","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650666"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650666","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650666","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5047441335","display_name":"Tong Zhu","orcid":"https://orcid.org/0000-0002-5278-8114"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tong Zhu","raw_affiliation_strings":["Beihang University,School of Software,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Software,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087082349","display_name":"Huobin Tan","orcid":"https://orcid.org/0000-0003-3113-6552"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huobin Tan","raw_affiliation_strings":["Beihang University,School of Software,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Software,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112371767","display_name":"Xinyu Chen","orcid":"https://orcid.org/0009-0008-6347-2490"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"XinYu Chen","raw_affiliation_strings":["Beihang University,School of Software,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Software,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043804622","display_name":"Yating Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yating Ren","raw_affiliation_strings":["Beihang University,School of Software,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Software,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047441335"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66524567,"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":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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":0.9966999888420105,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9962000250816345,"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":0.6340504288673401},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6291978359222412},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.4958890378475189},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.476644366979599},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47484973073005676},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.468070387840271},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2320498824119568},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12028089165687561}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6340504288673401},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6291978359222412},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4958890378475189},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.476644366979599},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47484973073005676},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.468070387840271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2320498824119568},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12028089165687561},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"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:10.1109/ijcnn60899.2024.10650666","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650666","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W68132019","https://openalex.org/W205829674","https://openalex.org/W1426956448","https://openalex.org/W2016753842","https://openalex.org/W2094728533","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2184957013","https://openalex.org/W2250184916","https://openalex.org/W2250342289","https://openalex.org/W2283196293","https://openalex.org/W2604165577","https://openalex.org/W2604314403","https://openalex.org/W2728059831","https://openalex.org/W2747329762","https://openalex.org/W2774837955","https://openalex.org/W2946476638","https://openalex.org/W2950393809","https://openalex.org/W2951105272","https://openalex.org/W2963380480","https://openalex.org/W2964152081","https://openalex.org/W2972167903","https://openalex.org/W2998496395","https://openalex.org/W3034374701","https://openalex.org/W3082429057","https://openalex.org/W3093752921","https://openalex.org/W3099387504","https://openalex.org/W3129482887","https://openalex.org/W3130909864","https://openalex.org/W3154252255","https://openalex.org/W3155001903","https://openalex.org/W3155919942","https://openalex.org/W3203679094","https://openalex.org/W4200000125","https://openalex.org/W4252707176","https://openalex.org/W4283208249","https://openalex.org/W4293363567","https://openalex.org/W4385245566","https://openalex.org/W4388776291","https://openalex.org/W6608344535","https://openalex.org/W6631964550","https://openalex.org/W6678830454","https://openalex.org/W6678846912","https://openalex.org/W6718112784","https://openalex.org/W6737547214","https://openalex.org/W6762867004","https://openalex.org/W6769589523","https://openalex.org/W6772406930","https://openalex.org/W6784989493"],"related_works":["https://openalex.org/W2604454537","https://openalex.org/W2808284704","https://openalex.org/W2897702399","https://openalex.org/W4206028705","https://openalex.org/W2757431232","https://openalex.org/W2954554213","https://openalex.org/W4206547516","https://openalex.org/W4293236197","https://openalex.org/W2932872266","https://openalex.org/W2054026175"],"abstract_inverted_index":{"Many":[0],"existing":[1,44,167],"knowledge":[2,64],"graph":[3,12,16,35,65,70,80,106,118,127],"embedding":[4,66],"methods":[5,19],"achieve":[6],"outstanding":[7],"performance":[8,160],"by":[9,82,103],"exploiting":[10],"the":[11,22,26,30,39,43,50,91,111,126,141,159,170],"structure,":[13],"among":[14],"which":[15],"neural":[17,36],"network-based":[18],"that":[20,68,151,164],"utilize":[21],"local":[23,73,112],"neighborhood":[24,74,113],"are":[25,98],"most":[27,166],"representative.":[28],"However,":[29],"shallow":[31],"network":[32],"structure":[33],"of":[34,46,161,165,172],"networks":[37],"limits":[38],"model\u2019s":[40,142],"expressiveness,":[41],"and":[42,72,157],"problem":[45],"over-smoothing":[47],"also":[48],"prevents":[49],"model":[51],"from":[52,90],"capturing":[53],"long-distance":[54,146],"information.":[55,147],"To":[56],"address":[57],"these":[58,95],"issues,":[59],"we":[60],"propose":[61],"a":[62,84,104,131],"Transformer-based":[63,105],"method":[67],"combines":[69],"paths":[71,81,97],"(TKGE-PN).":[75],"First,":[76],"it":[77],"samples":[78],"multiple":[79],"using":[83],"biased":[85],"random":[86],"walk":[87],"algorithm":[88],"starting":[89],"central":[92],"entity.":[93],"Then":[94],"sampled":[96],"transformed":[99],"into":[100],"vector":[101,120],"representations":[102,121],"path":[107,119,128],"encoding":[108,114,129],"module.":[109],"Finally,":[110],"module":[115],"aggregates":[116],"all":[117],"to":[122,139,144],"score":[123],"triples.":[124],"During":[125],"process,":[130],"masked":[132],"entity":[133],"relation":[134],"prediction":[135],"task":[136],"is":[137],"used":[138],"enhance":[140],"ability":[143],"learn":[145],"Experimental":[148],"results":[149],"show":[150],"on":[152],"two":[153],"standard":[154],"datasets,":[155],"FB15k-237":[156],"WN18RR,":[158],"TKGE-PN":[162],"surpasses":[163],"models,":[168],"demonstrating":[169],"effectiveness":[171],"our":[173],"approach.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
