{"id":"https://openalex.org/W4402352482","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650504","title":"LengthPath: The Length Reward of Knowledge Graph Reasoning Based on Deep Reinforcement Learning","display_name":"LengthPath: The Length Reward of Knowledge Graph Reasoning Based on Deep Reinforcement Learning","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352482","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650504"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650504","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650504","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/A5107079593","display_name":"Xu Ling-Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xu Ling-Xiao","raw_affiliation_strings":["SiChuan Normal University,School of Computer Science,ChengDu,China"],"affiliations":[{"raw_affiliation_string":"SiChuan Normal University,School of Computer Science,ChengDu,China","institution_ids":["https://openalex.org/I63354593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009172382","display_name":"Feng Lin","orcid":"https://orcid.org/0000-0002-6831-4458"},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Lin","raw_affiliation_strings":["SiChuan Normal University,School of Computer Science,ChengDu,China"],"affiliations":[{"raw_affiliation_string":"SiChuan Normal University,School of Computer Science,ChengDu,China","institution_ids":["https://openalex.org/I63354593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105098104","display_name":"Li Zi-Hao","orcid":"https://orcid.org/0009-0006-2696-0722"},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zi-Hao","raw_affiliation_strings":["SiChuan Normal University,School of Computer Science,ChengDu,China"],"affiliations":[{"raw_affiliation_string":"SiChuan Normal University,School of Computer Science,ChengDu,China","institution_ids":["https://openalex.org/I63354593"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yue Ling","orcid":null},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Ling","raw_affiliation_strings":["SiChuan Normal University,School of Computer Science,ChengDu,China"],"affiliations":[{"raw_affiliation_string":"SiChuan Normal University,School of Computer Science,ChengDu,China","institution_ids":["https://openalex.org/I63354593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107079594","display_name":"Shuai Qiu-Ping","orcid":null},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Qiu-Ping","raw_affiliation_strings":["SiChuan Normal University,School of Computer Science,ChengDu,China"],"affiliations":[{"raw_affiliation_string":"SiChuan Normal University,School of Computer Science,ChengDu,China","institution_ids":["https://openalex.org/I63354593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107079595","display_name":"Li Jie-Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Jie-Wei","raw_affiliation_strings":["SiChuan Normal University,School of Computer Science,ChengDu,China"],"affiliations":[{"raw_affiliation_string":"SiChuan Normal University,School of Computer Science,ChengDu,China","institution_ids":["https://openalex.org/I63354593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5107079593"],"corresponding_institution_ids":["https://openalex.org/I63354593"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12719083,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":null,"first_page":"1","last_page":"8"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.982699990272522,"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/T10028","display_name":"Topic Modeling","score":0.9811000227928162,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7512484788894653},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6876736283302307},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5053380131721497},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4881656765937805},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32660967111587524},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1841180920600891}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7512484788894653},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6876736283302307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5053380131721497},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4881656765937805},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32660967111587524},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1841180920600891}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650504","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650504","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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W1498436455","https://openalex.org/W1533230146","https://openalex.org/W1689711448","https://openalex.org/W1924770834","https://openalex.org/W1977970897","https://openalex.org/W2029249040","https://openalex.org/W2127795553","https://openalex.org/W2151502664","https://openalex.org/W2184957013","https://openalex.org/W2249040818","https://openalex.org/W2250342289","https://openalex.org/W2250635077","https://openalex.org/W2283196293","https://openalex.org/W2728059831","https://openalex.org/W2769099080","https://openalex.org/W2950393809","https://openalex.org/W2951105272","https://openalex.org/W2951515642","https://openalex.org/W2962886429","https://openalex.org/W2970618565","https://openalex.org/W3017152929","https://openalex.org/W3035251962","https://openalex.org/W3082320588","https://openalex.org/W3109305417","https://openalex.org/W4241811150","https://openalex.org/W4297733535","https://openalex.org/W6608344535","https://openalex.org/W6631964550","https://openalex.org/W6640212811","https://openalex.org/W6678830454","https://openalex.org/W6691327844","https://openalex.org/W6745779156","https://openalex.org/W6797625850"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Knowledge":[0,7],"Graph":[1,8],"(KG)":[2],"always":[3],"suffers":[4],"from":[5],"incompleteness.":[6],"Reasoning":[9],"(KGR)":[10],"aims":[11],"to":[12,65,81,100,126],"predict":[13],"the":[14,33,39,59,68,72,76,83,90,97,110,131,135,145],"unknown":[15],"entity":[16,114,133],"or":[17],"find":[18],"reasoning":[19,28,36,86],"paths":[20,87],"for":[21,159],"relations":[22],"over":[23],"incomplete":[24],"KG.":[25],"However,":[26],"multi-hop":[27],"still":[29],"facing":[30],"challenges,":[31],"because":[32],"process":[34],"of":[35,52,85,108,113,130,147],"usually":[37],"experiences":[38],"neighbor":[40,60,91,128],"information":[41,61,92,99,112],"issue.":[42],"Prior":[43],"works":[44],"just":[45],"use":[46,89],"path":[47],"efficiency":[48],"as":[49],"a":[50,122,155],"part":[51],"reward":[53,74,102],"function":[54],"and":[55,88,116,134,142,150],"do":[56],"not":[57],"utilize":[58],"effectively.":[62,93],"In":[63],"order":[64],"deal":[66],"with":[67],"situation,":[69],"we":[70,120],"propose":[71,121],"length":[73,84],"in":[75],"Reinforcement":[77],"Learning":[78],"(RL)":[79],"framework":[80,125],"represent":[82],"Our":[94],"model":[95,149,152],"utilizes":[96],"position":[98],"design":[101],"function.":[103],"To":[104],"solve":[105],"this":[106],"problem":[107],"save":[109],"semantic":[111],"neighbors":[115],"historical":[117],"trajectory":[118],"information,":[119],"new":[123],"GRU-GAT":[124],"capture":[127],"feature":[129],"current":[132],"target":[136],"entity.":[137],"Experimental":[138],"results":[139],"on":[140],"NELL-995":[141],"FB15K237":[143],"demonstrate":[144],"effectiveness":[146],"our":[148,151],"can":[153],"identify":[154],"more":[156],"balanced":[157],"route":[158],"every":[160],"relation.":[161]},"counts_by_year":[],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}