{"id":"https://openalex.org/W3015148890","doi":"https://doi.org/10.1145/3318464.3386145","title":"GIANT: Scalable Creation of a Web-scale Ontology","display_name":"GIANT: Scalable Creation of a Web-scale Ontology","publication_year":2020,"publication_date":"2020-05-29","ids":{"openalex":"https://openalex.org/W3015148890","doi":"https://doi.org/10.1145/3318464.3386145","mag":"3015148890"},"language":"en","primary_location":{"id":"doi:10.1145/3318464.3386145","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3318464.3386145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.02118","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Bang Liu","raw_affiliation_strings":["University of Alberta, Edmonton, AB, Canada"],"affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton, AB, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Weidong Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weidong Guo","raw_affiliation_strings":["Tencent, Shenzhen, AB, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, AB, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Di Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Di Niu","raw_affiliation_strings":["University of Alberta, Edmonton, AB, Canada"],"affiliations":[{"raw_affiliation_string":"University of Alberta, Edmonton, AB, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jinwen Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinwen Luo","raw_affiliation_strings":["Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chaoyue Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoyue Wang","raw_affiliation_strings":["Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhen Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Wen","raw_affiliation_strings":["Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yu Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Xu","raw_affiliation_strings":["Tencent, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154425047"],"apc_list":null,"apc_paid":null,"fwci":1.2342,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.83777317,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"393","last_page":"409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991999864578247,"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/T10028","display_name":"Topic Modeling","score":0.9991999864578247,"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":0.9962000250816345,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9962000250816345,"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/ontology","display_name":"Ontology","score":0.7915999889373779},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5873000025749207},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.5353000164031982},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.48500001430511475},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.475600004196167},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.45890000462532043},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.43220001459121704},{"id":"https://openalex.org/keywords/suggested-upper-merged-ontology","display_name":"Suggested Upper Merged Ontology","score":0.43059998750686646},{"id":"https://openalex.org/keywords/upper-ontology","display_name":"Upper ontology","score":0.42489999532699585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8396999835968018},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.7915999889373779},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.5895000100135803},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5873000025749207},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5437999963760376},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.5353000164031982},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.48500001430511475},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.475600004196167},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.45890000462532043},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C50971890","wikidata":"https://www.wikidata.org/wiki/Q7635093","display_name":"Suggested Upper Merged Ontology","level":4,"score":0.43059998750686646},{"id":"https://openalex.org/C78726541","wikidata":"https://www.wikidata.org/wiki/Q3882785","display_name":"Upper ontology","level":3,"score":0.42489999532699585},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3847000002861023},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.38420000672340393},{"id":"https://openalex.org/C50382505","wikidata":"https://www.wikidata.org/wiki/Q2553356","display_name":"OWL-S","level":4,"score":0.38199999928474426},{"id":"https://openalex.org/C22550185","wikidata":"https://www.wikidata.org/wiki/Q7095047","display_name":"Ontology-based data integration","level":3,"score":0.37869998812675476},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C137003198","wikidata":"https://www.wikidata.org/wiki/Q7247296","display_name":"Process ontology","level":3,"score":0.3736000061035156},{"id":"https://openalex.org/C61673122","wikidata":"https://www.wikidata.org/wiki/Q7095059","display_name":"Ontology language","level":3,"score":0.35370001196861267},{"id":"https://openalex.org/C115408247","wikidata":"https://www.wikidata.org/wiki/Q3882789","display_name":"Ontology Inference Layer","level":5,"score":0.3433000147342682},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.28630000352859497},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C98893333","wikidata":"https://www.wikidata.org/wiki/Q4339878","display_name":"Ontology alignment","level":4,"score":0.2671999931335449},{"id":"https://openalex.org/C35578498","wikidata":"https://www.wikidata.org/wiki/Q193424","display_name":"Web service","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C137982476","wikidata":"https://www.wikidata.org/wiki/Q7072326","display_name":"Open Biomedical Ontologies","level":5,"score":0.25369998812675476}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3318464.3386145","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3318464.3386145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2004.02118","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.02118","pdf_url":"https://arxiv.org/pdf/2004.02118","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":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":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2004.02118","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.02118","pdf_url":"https://arxiv.org/pdf/2004.02118","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":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":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1552847225","https://openalex.org/W1890727290","https://openalex.org/W1967315849","https://openalex.org/W1976146430","https://openalex.org/W1988059434","https://openalex.org/W2017708378","https://openalex.org/W2020278455","https://openalex.org/W2022166150","https://openalex.org/W2029915058","https://openalex.org/W2049107599","https://openalex.org/W2053238041","https://openalex.org/W2066066594","https://openalex.org/W2086378526","https://openalex.org/W2100071287","https://openalex.org/W2108706252","https://openalex.org/W2120814856","https://openalex.org/W2123661878","https://openalex.org/W2138605095","https://openalex.org/W2150815390","https://openalex.org/W2151803977","https://openalex.org/W2153848201","https://openalex.org/W2168289837","https://openalex.org/W2171960770","https://openalex.org/W2223881431","https://openalex.org/W2250999640","https://openalex.org/W2251135946","https://openalex.org/W2251392452","https://openalex.org/W2469104253","https://openalex.org/W2475245295","https://openalex.org/W2512522169","https://openalex.org/W2539469848","https://openalex.org/W2539671052","https://openalex.org/W2562564313","https://openalex.org/W2593560537","https://openalex.org/W2618285232","https://openalex.org/W2620787630","https://openalex.org/W2768070595","https://openalex.org/W2788525741","https://openalex.org/W2804552794","https://openalex.org/W2804656660","https://openalex.org/W2946532448","https://openalex.org/W7061398984"],"related_works":[],"abstract_inverted_index":{"Understanding":[0],"what":[1],"online":[2,69,183],"users":[3],"may":[4],"pay":[5],"attention":[6],"to":[7,13,56,89,105,130,154,175],"on":[8,179],"the":[9,65,113,135,147,163,186,197],"web":[10,117],"is":[11,72],"key":[12],"content":[14,180],"recommendation":[15],"and":[16,27,35,41,49,62,119,145,150],"search":[17,120],"services.":[18],"These":[19],"services":[20],"will":[21],"benefit":[22],"from":[23,112],"a":[24,44,73,87,91,97,132,155,172],"highly":[25],"structured":[26,75,94],"web-scale":[28],"ontology":[29,76,187],"of":[30,47,68,100,116,125,157,185],"entities,":[31],"concepts,":[32,60],"events,":[33],"topics":[34,63],"categories.":[36],"While":[37],"existing":[38],"knowledge":[39],"bases":[40],"taxonomies":[42],"embody":[43],"large":[45,98],"volume":[46,115],"entities":[48],"categories,":[50],"we":[51,84],"argue":[52],"that":[53,192],"they":[54],"fail":[55],"discover":[57],"properly":[58],"grained":[59],"events":[61],"in":[64,134,143,167,200],"language":[66,102],"style":[67],"users.":[70],"Neither":[71],"logically":[74],"maintained":[77],"among":[78],"these":[79],"notions.":[80],"In":[81],"this":[82],"paper,":[83],"present":[85,138],"GIANT,":[86,144],"mechanism":[88],"construct":[90],"user-centered,":[92],"web-scale,":[93],"ontology,":[95],"containing":[96],"number":[99],"natural":[101],"phrases":[103],"conforming":[104],"user":[106],"attentions":[107],"at":[108],"various":[109],"granularities,":[110],"mined":[111],"vast":[114],"documents":[118],"click":[121],"logs.":[122],"Various":[123],"types":[124],"edges":[126],"are":[127],"also":[128],"constructed":[129],"maintain":[131],"hierarchy":[133],"ontology.":[136],"We":[137],"our":[139],"detailed":[140],"techniques":[141],"used":[142],"evaluate":[146],"proposed":[148],"models":[149],"methods":[151],"as":[152,159,161],"compared":[153],"variety":[156],"baselines,":[158],"well":[160],"deploy":[162],"resulted":[164],"Attention":[165],"Ontology":[166],"real-world":[168],"applications,":[169],"involving":[170],"over":[171],"billion":[173],"users,":[174],"observe":[176],"its":[177],"effect":[178],"recommendation.":[181,203],"The":[182],"performance":[184],"built":[188],"by":[189],"GIANT":[190],"proves":[191],"it":[193],"can":[194],"significantly":[195],"improve":[196],"click-through":[198],"rate":[199],"news":[201],"feeds":[202]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-04-10T00:00:00"}
