{"id":"https://openalex.org/W4307592335","doi":"https://doi.org/10.48550/arxiv.2205.06456","title":"Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning","display_name":"Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4307592335","doi":"https://doi.org/10.48550/arxiv.2205.06456"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2205.06456","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2205.06456","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100376803","display_name":"Huijuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Huijuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102725869","display_name":"Siming Dai","orcid":"https://orcid.org/0000-0001-7537-5403"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Siming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042388009","display_name":"Weiyue Su","orcid":"https://orcid.org/0000-0002-6417-3221"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Weiyue","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101641973","display_name":"Hui Zhong","orcid":"https://orcid.org/0000-0002-8782-4963"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Hui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090453650","display_name":"Zeyang Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Zeyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062586768","display_name":"Zhengjie Huang","orcid":"https://orcid.org/0000-0001-6298-8112"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Zhengjie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005049423","display_name":"Shikun Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Shikun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100662054","display_name":"Zeyu Chen","orcid":"https://orcid.org/0000-0001-6286-0581"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zeyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107072461","display_name":"Yu Sun","orcid":"https://orcid.org/0000-0003-3525-2753"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5084155236","display_name":"Dianhai Yu","orcid":"https://orcid.org/0000-0002-0163-2603"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Dianhai","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.609064,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":60,"max":70},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9992,"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":0.9992,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9107,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/encode","display_name":"ENCODE","score":0.5246004},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph Embedding","score":0.52038586},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature Learning","score":0.43802726},{"id":"https://openalex.org/keywords/representation","display_name":"Representation","score":0.4273302},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.41020128}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.81333613},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.71663785},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.67519283},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.59494454},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5669544},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.54528403},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5246004},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.52038586},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4864235},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.47938538},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.43802726},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4273302},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.41020128},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38158566},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24971247},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.17638102},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2205.06456","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.06456","pdf_url":"http://arxiv.org/pdf/2205.06456","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2205.06456","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2205.06456","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3206528106","https://openalex.org/W3038102983","https://openalex.org/W3036264823","https://openalex.org/W2950907416","https://openalex.org/W2932872266","https://openalex.org/W2912814903","https://openalex.org/W2123605750","https://openalex.org/W2088740331","https://openalex.org/W2082479932","https://openalex.org/W1559483280"],"abstract_inverted_index":{"Relational":[0],"graph":[1,10,30,61],"neural":[2],"networks":[3],"have":[4],"garnered":[5],"particular":[6],"attention":[7],"to":[8,25,55,95,138,148],"encode":[9],"context":[11,31,74,79,85,97],"in":[12,65],"knowledge":[13],"graphs":[14],"(KGs).":[15],"Although":[16],"they":[17],"achieved":[18],"competitive":[19],"performance":[20],"on":[21,142],"small":[22],"KGs,":[23],"how":[24],"efficiently":[26],"and":[27,75,144],"effectively":[28],"utilize":[29],"for":[32],"large":[33],"KGs":[34,66],"remains":[35],"an":[36],"open":[37],"problem.":[38],"To":[39],"this":[40],"end,":[41],"we":[42,69,82,92],"propose":[43],"the":[44,71,76,153],"Relation-based":[45],"Embedding":[46],"Propagation":[47],"(REP)":[48],"method.":[49],"It":[50],"is":[51],"a":[52],"post-processing":[53],"technique":[54],"adapt":[56],"pre-trained":[57],"KG":[58],"embeddings":[59],"with":[60,87],"context.":[62],"As":[63],"relations":[64],"are":[67],"directional,":[68],"model":[70],"incoming":[72],"head":[73],"outgoing":[77],"tail":[78],"separately.":[80],"Accordingly,":[81],"design":[83],"relational":[84],"functions":[86],"no":[88],"external":[89],"parameters.":[90],"Besides,":[91],"use":[93],"averaging":[94],"aggregate":[96],"information,":[98],"making":[99],"REP":[100,120],"more":[101],"computation-efficient.":[102],"We":[103],"theoretically":[104],"prove":[105],"that":[106,119],"such":[107],"designs":[108],"can":[109],"avoid":[110],"information":[111],"distortion":[112],"during":[113],"propagation.":[114],"Extensive":[115],"experiments":[116],"also":[117],"demonstrate":[118],"has":[121],"significant":[122],"scalability":[123],"while":[124],"improving":[125],"or":[126],"maintaining":[127],"prediction":[128],"quality.":[129],"Notably,":[130],"it":[131],"averagely":[132],"brings":[133],"about":[134],"10%":[135],"relative":[136],"improvement":[137],"triplet-based":[139],"embedding":[140],"methods":[141],"OGBL-WikiKG2":[143],"takes":[145],"5%-83%":[146],"time":[147],"achieve":[149],"comparable":[150],"results":[151],"as":[152],"state-of-the-art":[154],"GC-OTE.":[155]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4307592335","counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-01-20T07:19:31.655694","created_date":"2022-11-03"}