{"id":"https://openalex.org/W2908230750","doi":"https://doi.org/10.1145/3289600.3290956","title":"Knowledge Graph Embedding Based Question Answering","display_name":"Knowledge Graph Embedding Based Question Answering","publication_year":2019,"publication_date":"2019-01-30","ids":{"openalex":"https://openalex.org/W2908230750","doi":"https://doi.org/10.1145/3289600.3290956","mag":"2908230750"},"language":"en","primary_location":{"id":"doi:10.1145/3289600.3290956","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3289600.3290956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5055037545","display_name":"Xiao Huang","orcid":"https://orcid.org/0000-0002-3867-900X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Huang","raw_affiliation_strings":["Baidu Research, Sunnyvale, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Research, Sunnyvale, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100639601","display_name":"Jingyuan Zhang","orcid":"https://orcid.org/0000-0001-6644-4673"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingyuan Zhang","raw_affiliation_strings":["Baidu Research, Sunnyvale, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Research, Sunnyvale, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103234379","display_name":"Dingcheng Li","orcid":"https://orcid.org/0000-0002-1630-4338"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dingcheng Li","raw_affiliation_strings":["Baidu Research, Bellevue, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Research, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100435461","display_name":"Ping Li","orcid":"https://orcid.org/0000-0001-7045-0945"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ping Li","raw_affiliation_strings":["Baidu Research, Bellevue, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Research, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":576,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"105","last_page":"113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T10028","display_name":"Topic Modeling","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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9995999932289124,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9898999929428101,"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/question-answering","display_name":"Question answering","score":0.8354151248931885},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7652263641357422},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6363208889961243},{"id":"https://openalex.org/keywords/predicate","display_name":"Predicate (mathematical logic)","score":0.5931458473205566},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5711984038352966},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5515583753585815},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5392398238182068},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5200350284576416},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.48286178708076477},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.40581411123275757},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.0970369279384613}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8354151248931885},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7652263641357422},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6363208889961243},{"id":"https://openalex.org/C140146324","wikidata":"https://www.wikidata.org/wiki/Q1144319","display_name":"Predicate (mathematical logic)","level":2,"score":0.5931458473205566},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5711984038352966},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5515583753585815},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5392398238182068},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5200350284576416},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.48286178708076477},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.40581411123275757},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0970369279384613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3289600.3290956","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3289600.3290956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1120956970","https://openalex.org/W1426956448","https://openalex.org/W1552847225","https://openalex.org/W1801721664","https://openalex.org/W1894439495","https://openalex.org/W1988157164","https://openalex.org/W2022166150","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2131726681","https://openalex.org/W2158028897","https://openalex.org/W2184957013","https://openalex.org/W2247119764","https://openalex.org/W2250539671","https://openalex.org/W2250807343","https://openalex.org/W2251079237","https://openalex.org/W2251182116","https://openalex.org/W2251287417","https://openalex.org/W2251289180","https://openalex.org/W2252136820","https://openalex.org/W2283196293","https://openalex.org/W2341820192","https://openalex.org/W2397801432","https://openalex.org/W2499696929","https://openalex.org/W2509893387","https://openalex.org/W2522332826","https://openalex.org/W2571811098","https://openalex.org/W2572289264","https://openalex.org/W2583923862","https://openalex.org/W2585270596","https://openalex.org/W2605089588","https://openalex.org/W2739716023","https://openalex.org/W2759136286","https://openalex.org/W2759281352","https://openalex.org/W2778810234","https://openalex.org/W2895988667","https://openalex.org/W2896391192","https://openalex.org/W2898342438","https://openalex.org/W2913979215","https://openalex.org/W2950133940","https://openalex.org/W2951723246","https://openalex.org/W2963031827","https://openalex.org/W2963655104","https://openalex.org/W2963688791","https://openalex.org/W2963800216","https://openalex.org/W2963861211","https://openalex.org/W2963897632","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W3157284875","https://openalex.org/W4401140950","https://openalex.org/W2147241511","https://openalex.org/W2259406085","https://openalex.org/W2099715052","https://openalex.org/W4226247999","https://openalex.org/W1516679419","https://openalex.org/W190396239","https://openalex.org/W3090872036","https://openalex.org/W4400650325"],"abstract_inverted_index":{"Question":[0,166],"answering":[1,173],"over":[2],"knowledge":[3,12,34,63],"graph":[4,13,64],"(QA-KG)":[5],"aims":[6],"to":[7,15,74,112,115],"use":[8,113],"facts":[9],"in":[10,35,87,132,135,183,233,251],"the":[11,30,36,50,84,88,117,140,149,157,174,191,224,234,245,252,257,266,270],"(KG)":[14],"answer":[16,206],"natural":[17,54,136],"language":[18,55,137],"questions.":[19,138],"It":[20],"helps":[21],"end":[22],"users":[23],"more":[24,27],"efficiently":[25],"and":[26,32,104,145,199,216,229],"easily":[28],"access":[29],"substantial":[31],"valuable":[33],"KG,":[37],"without":[38],"knowing":[39],"its":[40,195,213],"data":[41],"structures.":[42],"QA-KG":[43,118,272],"is":[44,56,73,254],"a":[45,59,79,123,127,207,240,261],"nontrivial":[46],"problem":[47],"since":[48,126],"capturing":[49],"semantic":[51],"meaning":[52],"of":[53,142,151,178,211],"difficult":[57],"for":[58],"machine.":[60],"Meanwhile,":[61],"many":[62],"embedding":[65,236],"methods":[66],"have":[67],"been":[68],"proposed.":[69],"The":[70,93],"key":[71],"idea":[72],"represent":[75],"each":[76,185],"predicate/entity":[77],"as":[78,101,256],"low-dimensional":[80],"vector,":[81],"such":[82,100],"that":[83,265],"relation":[85],"information":[86],"KG":[89,102,235,253],"could":[90,96,129,187],"be":[91,130,188],"preserved.":[92],"learned":[94,247],"vectors":[95],"benefit":[97],"various":[98],"applications":[99],"completion":[103],"recommender":[105],"systems.":[106],"In":[107],"this":[108,121],"paper,":[109],"we":[110,159],"explore":[111],"them":[114],"handle":[116],"problem.":[119],"However,":[120],"remains":[122],"challenging":[124],"task":[125],"predicate":[128,201,217],"expressed":[131],"different":[133],"ways":[134],"Also,":[139],"ambiguity":[141],"entity":[143,198,215,231],"names":[144,147],"partial":[146],"makes":[148],"number":[150],"possible":[152],"answers":[153],"large.":[154],"To":[155,205],"bridge":[156],"gap,":[158],"propose":[160],"an":[161],"effective":[162],"Knowledge":[163],"Embedding":[164],"based":[165],"Answering":[167],"(KEQA)":[168],"framework.":[169],"We":[170],"focus":[171],"on":[172,239,260],"most":[175],"common":[176],"types":[177],"questions,":[179,182],"i.e.,":[180],"simple":[181,208],"which":[184],"question":[186],"answered":[189],"by":[190],"machine":[192],"straightforwardly":[193],"if":[194],"single":[196,200],"head":[197,214,226],"are":[202],"correctly":[203],"identified.":[204],"question,":[209],"instead":[210],"inferring":[212],"directly,":[218],"KEQA":[219,268],"targets":[220],"at":[221],"jointly":[222],"recovering":[223],"question's":[225],"entity,":[227],"predicate,":[228],"tail":[230],"representations":[232],"spaces.":[237],"Based":[238],"carefully-designed":[241],"joint":[242],"distance":[243],"metric,":[244],"three":[246],"vectors'":[248],"closest":[249],"fact":[250],"returned":[255],"answer.":[258],"Experiments":[259],"widely-adopted":[262],"benchmark":[263],"demonstrate":[264],"proposed":[267],"outperforms":[269],"state-of-the-art":[271],"methods.":[273]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":67},{"year":2024,"cited_by_count":95},{"year":2023,"cited_by_count":98},{"year":2022,"cited_by_count":98},{"year":2021,"cited_by_count":116},{"year":2020,"cited_by_count":71},{"year":2019,"cited_by_count":18},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2025-10-10T00:00:00"}