{"id":"https://openalex.org/W3014490432","doi":null,"title":"Improving the Utility of Knowledge Graph Embeddings with Calibration.","display_name":"Improving the Utility of Knowledge Graph Embeddings with Calibration.","publication_year":2020,"publication_date":"2020-04-02","ids":{"openalex":"https://openalex.org/W3014490432","mag":"3014490432"},"language":"en","primary_location":{"id":"mag:3014490432","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2004.01168","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2004.01168","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019130511","display_name":"Tara Safavi","orcid":"https://orcid.org/0000-0002-3553-4331","observed_orcids":["https://orcid.org/0000-0002-3553-4331"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tara Safavi","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3553-4331","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015996266","display_name":"Danai Koutra","orcid":"https://orcid.org/0000-0002-3206-8179","observed_orcids":["https://orcid.org/0000-0002-3206-8179"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Danai Koutra","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3206-8179","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031940626","display_name":"Edgar Meij","orcid":"https://orcid.org/0000-0003-0516-3688","observed_orcids":["https://orcid.org/0000-0003-0516-3688"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Edgar Meij","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-0516-3688","affiliations":[]}],"institutions":[],"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":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994999766349792,"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":0.9994999766349792,"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":0.0003000000142492354,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/knowledge-graph-embedding","display_name":"knowledge graph embedding","score":0.9620000123977661},{"id":"https://openalex.org/keywords/calibration","display_name":"calibration","score":0.6570000052452087},{"id":"https://openalex.org/keywords/confidence-estimation","display_name":"confidence estimation","score":0.8920000195503235},{"id":"https://openalex.org/keywords/link-prediction","display_name":"link prediction","score":0.8410000205039978},{"id":"https://openalex.org/keywords/knowledge-graph-completion","display_name":"knowledge graph completion","score":0.9789999723434448},{"id":"https://openalex.org/keywords/prediction-reliability","display_name":"forecast reliability","score":0.48500001430511475},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"crowdsourcing","score":0.7440000176429749},{"id":"https://openalex.org/keywords/wikidata","display_name":"Wikidata","score":0.6489999890327454},{"id":"https://openalex.org/keywords/incomplete-knowledge-graphs","display_name":"incomplete knowledge graphs","score":0.7250000238418579}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7718965411186218},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.7658460140228271},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.7479326725006104},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6968267560005188},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6543068885803223},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6511088013648987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5132991671562195},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46363967657089233},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4438944458961487},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3749140799045563},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35805246233940125},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0822252631187439},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3014490432","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2004.01168","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3014490432","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2004.01168","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1529533208","https://openalex.org/W1533230146","https://openalex.org/W1618905105","https://openalex.org/W1975879668","https://openalex.org/W1992549066","https://openalex.org/W2016753842","https://openalex.org/W2022166150","https://openalex.org/W2080133951","https://openalex.org/W2081580037","https://openalex.org/W2098824882","https://openalex.org/W2128407051","https://openalex.org/W2250184916","https://openalex.org/W2283196293","https://openalex.org/W2432356473","https://openalex.org/W2572179331","https://openalex.org/W2604165577","https://openalex.org/W2626967530","https://openalex.org/W2759136286","https://openalex.org/W2794079986","https://openalex.org/W2801930304","https://openalex.org/W2889583850","https://openalex.org/W2920008855","https://openalex.org/W2945623882","https://openalex.org/W2951561177","https://openalex.org/W2963485453","https://openalex.org/W2970723181","https://openalex.org/W2971045469","https://openalex.org/W2994634959","https://openalex.org/W6950194991"],"related_works":["https://openalex.org/W3087439297","https://openalex.org/W2994634959","https://openalex.org/W3115640131","https://openalex.org/W4416035508","https://openalex.org/W4320507223","https://openalex.org/W3032390337","https://openalex.org/W3048357673","https://openalex.org/W4386429958","https://openalex.org/W7214304275","https://openalex.org/W6945035169"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"machine":[3],"learning":[4],"models":[5,52,86],"that":[6,35,100],"embed":[7],"knowledge":[8,27,59,83,106,117,140],"graph":[9,60,84,107,118,141],"entities":[10],"and":[11,113,137],"relationships":[12],"toward":[13],"the":[14,134],"goal":[15],"of":[16,133],"predicting":[17],"unseen":[18],"triples,":[19],"which":[20],"is":[21,65],"an":[22],"important":[23],"task":[24],"because":[25,63],"most":[26],"graphs":[28],"are":[29],"by":[30],"nature":[31],"incomplete.":[32],"We":[33,121],"posit":[34],"while":[36],"offline":[37],"link":[38],"prediction":[39],"accuracy":[40],"using":[41],"embeddings":[42,108],"has":[43],"been":[44],"steadily":[45],"improving":[46],"on":[47],"benchmark":[48,136],"datasets,":[49],"such":[50],"embedding":[51,85],"have":[53],"limited":[54],"practical":[55],"utility":[56],"in":[57,116],"real-world":[58],"completion":[61,119],"tasks":[62],"it":[64],"not":[66],"clear":[67],"when":[68],"their":[69],"predictions":[70],"should":[71],"be":[72],"accepted":[73],"or":[74],"trusted.":[75],"To":[76],"this":[77],"end,":[78],"we":[79,98],"propose":[80],"to":[81,87,111],"calibrate":[82],"output":[88],"reliable":[89],"confidence":[90,102],"estimates":[91],"for":[92],"predicted":[93],"triples.":[94],"In":[95],"crowdsourcing":[96],"experiments,":[97],"demonstrate":[99],"calibrated":[101],"scores":[103],"can":[104],"make":[105],"more":[109],"useful":[110],"practitioners":[112],"data":[114],"annotators":[115],"tasks.":[120],"also":[122],"release":[123],"two":[124],"resources":[125],"from":[126,144],"our":[127],"evaluation":[128],"tasks:":[129],"An":[130],"enriched":[131],"version":[132],"FB15K":[135],"a":[138],"new":[139],"dataset":[142],"extracted":[143],"Wikidata.":[145]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2020-04-10T00:00:00"}
