{"id":"https://openalex.org/W3012846531","doi":"https://doi.org/10.1145/3366423.3380257","title":"Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction","display_name":"Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012846531","doi":"https://doi.org/10.1145/3366423.3380257","mag":"3012846531"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380257","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380257","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380257","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103045601","display_name":"Paolo Rosso","orcid":"https://orcid.org/0009-0008-7739-5668"},"institutions":[{"id":"https://openalex.org/I154338468","display_name":"University of Fribourg","ror":"https://ror.org/022fs9h90","country_code":"CH","type":"education","lineage":["https://openalex.org/I154338468"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Paolo Rosso","raw_affiliation_strings":["University of Fribourg, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of Fribourg, Switzerland","institution_ids":["https://openalex.org/I154338468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026625771","display_name":"Dingqi Yang","orcid":"https://orcid.org/0000-0002-6831-0422"},"institutions":[{"id":"https://openalex.org/I154338468","display_name":"University of Fribourg","ror":"https://ror.org/022fs9h90","country_code":"CH","type":"education","lineage":["https://openalex.org/I154338468"]},{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["CH","MO"],"is_corresponding":false,"raw_author_name":"Dingqi Yang","raw_affiliation_strings":["University of Fribourg, Switzerland and University of Macau, SAR China"],"affiliations":[{"raw_affiliation_string":"University of Fribourg, Switzerland and University of Macau, SAR China","institution_ids":["https://openalex.org/I204512498","https://openalex.org/I154338468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028454093","display_name":"Philippe Cudr\u00e9-Mauroux","orcid":"https://orcid.org/0000-0003-2588-4212"},"institutions":[{"id":"https://openalex.org/I154338468","display_name":"University of Fribourg","ror":"https://ror.org/022fs9h90","country_code":"CH","type":"education","lineage":["https://openalex.org/I154338468"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Philippe Cudr\u00e9-Mauroux","raw_affiliation_strings":["University of Fribourg, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of Fribourg, Switzerland","institution_ids":["https://openalex.org/I154338468"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103045601"],"corresponding_institution_ids":["https://openalex.org/I154338468"],"apc_list":null,"apc_paid":null,"fwci":11.9185,"has_fulltext":false,"cited_by_count":141,"citation_normalized_percentile":{"value":0.98841196,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1885","last_page":"1896"},"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":0.9797999858856201,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9736999869346619,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7625506520271301},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.6423097848892212},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6313261985778809},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.590893566608429},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5532521605491638},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5382978320121765},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5337864756584167},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5042845010757446},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.5019476413726807},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.485087513923645},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.4421499967575073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32910698652267456},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.30653616786003113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25386250019073486}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7625506520271301},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.6423097848892212},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6313261985778809},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.590893566608429},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5532521605491638},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5382978320121765},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5337864756584167},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5042845010757446},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.5019476413726807},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.485087513923645},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4421499967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32910698652267456},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30653616786003113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25386250019073486},{"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"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:10.1145/3366423.3380257","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380257","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380257","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380257","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W1529533208","https://openalex.org/W1533230146","https://openalex.org/W1540618313","https://openalex.org/W1602136775","https://openalex.org/W1986321089","https://openalex.org/W2094728533","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2128407051","https://openalex.org/W2146502635","https://openalex.org/W2158028897","https://openalex.org/W2163605009","https://openalex.org/W2184957013","https://openalex.org/W2250342289","https://openalex.org/W2250635077","https://openalex.org/W2250807343","https://openalex.org/W2250930514","https://openalex.org/W2251079237","https://openalex.org/W2283196293","https://openalex.org/W2343431530","https://openalex.org/W2509435444","https://openalex.org/W2509893387","https://openalex.org/W2604165577","https://openalex.org/W2604314403","https://openalex.org/W2613223139","https://openalex.org/W2728059831","https://openalex.org/W2759136286","https://openalex.org/W2787882523","https://openalex.org/W2788798739","https://openalex.org/W2896161497","https://openalex.org/W2914812111","https://openalex.org/W2918636532","https://openalex.org/W2949117887","https://openalex.org/W2951006102","https://openalex.org/W2963203544","https://openalex.org/W2963224980","https://openalex.org/W2963432357","https://openalex.org/W2963855739","https://openalex.org/W2963870853","https://openalex.org/W2964161331","https://openalex.org/W2964279602","https://openalex.org/W3007162611"],"related_works":["https://openalex.org/W4234874385","https://openalex.org/W1518185400","https://openalex.org/W2081900870","https://openalex.org/W3200586296","https://openalex.org/W2323648130","https://openalex.org/W4230332972","https://openalex.org/W1998033311","https://openalex.org/W4247322236","https://openalex.org/W2157140558","https://openalex.org/W4231165741"],"abstract_inverted_index":{"Knowledge":[0],"Graph":[1],"(KG)":[2],"embeddings":[3,44],"are":[4,130],"a":[5,18,21,38,51,79,95,107,115,161,173],"powerful":[6],"tool":[7],"for":[8,70,151],"predicting":[9],"missing":[10],"links":[11,31],"in":[12,65,68,125,143,172,187],"KGs.":[13,216],"Existing":[14],"techniques":[15,98],"typically":[16],"represent":[17],"KG":[19,163,185,225],"as":[20,128,138],"set":[22,116],"of":[23,61,117,132,183,208],"triplets,":[24,189],"where":[25,73],"each":[26,74,195],"triplet":[27,55,81,134,196],"(h,":[28,82],"r,":[29,40,83],"t)":[30],"two":[32],"entities":[33],"h":[34],"and":[35,41,146,197,240],"t":[36],"through":[37],"relation":[39],"learn":[42,101],"entity/relation":[43],"from":[45,102,169,229,247],"such":[46,50,103],"triplets":[47,230],"while":[48],"preserving":[49],"structure.":[52],"However,":[53],"this":[54,156],"representation":[56,113,253],"oversimplifies":[57],"the":[58,62,66,87,133,139,148,179,184,188,192,206,224,236,244,251],"complex":[59],"nature":[60],"data":[63,104,141],"stored":[64],"KG,":[67],"particular":[69],"hyper-relational":[71,108,162,170,248],"facts,":[72],"fact":[75,109],"contains":[76],"not":[77,177,222],"only":[78,120,178,223,231],"base":[80],"t),":[84],"but":[85,190,242],"also":[86,191,243],"associated":[88,199],"key-value":[89,118,200],"pairs":[90,119],"(k,":[91],"v).":[92],"Even":[93],"though":[94],"few":[96],"recent":[97],"tried":[99],"to":[100],"by":[105],"transforming":[106],"into":[110],"an":[111],"n-ary":[112,252],"(i.e.,":[114],"without":[121],"triplets),":[122],"they":[123,129],"result":[124],"suboptimal":[126],"models":[127],"unaware":[131],"structure,":[135],"which":[136,166],"serves":[137],"fundamental":[140],"structure":[142],"modern":[144],"KGs":[145],"preserves":[147],"essential":[149],"information":[150,182],"link":[152,212,237],"prediction.":[153],"To":[154],"address":[155],"issue,":[157],"we":[158],"propose":[159],"HINGE,":[160],"embedding":[164,226],"model,":[165],"directly":[167],"learns":[168],"facts":[171,249],"KG.":[174],"HINGE":[175,209,219],"captures":[176],"primary":[180],"structural":[181],"encoded":[186],"correlation":[193],"between":[194],"its":[198],"pairs.":[201],"Our":[202],"extensive":[203],"evaluation":[204],"shows":[205],"superiority":[207],"on":[210,235],"various":[211],"prediction":[213,238],"tasks":[214,239],"over":[215],"In":[217],"particular,":[218],"consistently":[220],"outperforms":[221],"methods":[227,245],"learning":[228,246],"(by":[232,254],"0.81-41.45%":[233],"depending":[234],"settings),":[241],"using":[250],"13.2-84.1%).":[255]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":29},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":37},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":5},{"year":2012,"cited_by_count":1}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}