{"id":"https://openalex.org/W1699691160","doi":"https://doi.org/10.3115/v1/n15-1054","title":"Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods","display_name":"Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W1699691160","doi":"https://doi.org/10.3115/v1/n15-1054","mag":"1699691160"},"language":"en","primary_location":{"id":"doi:10.3115/v1/n15-1054","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/n15-1054","pdf_url":"https://aclanthology.org/N15-1054.pdf","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 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/N15-1054.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043840749","display_name":"Arvind Neelakantan","orcid":null},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arvind Neelakantan","raw_affiliation_strings":["University of Massachusetts: Amherst#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts: Amherst#TAB#","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076904467","display_name":"Ming\u2010Wei Chang","orcid":"https://orcid.org/0000-0002-0137-8895"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ming-Wei Chang","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":73,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"515","last_page":"525"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9980000257492065,"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":0.9915000200271606,"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/computer-science","display_name":"Computer science","score":0.8110812902450562},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.8058381676673889},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.7572949528694153},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.7345362901687622},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6656659245491028},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6111290454864502},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.5359009504318237},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5239834189414978},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.46643733978271484},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.46386900544166565},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4585523307323456},{"id":"https://openalex.org/keywords/entity-linking","display_name":"Entity linking","score":0.4478800296783447},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4372815787792206},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.43004685640335083},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.395570307970047},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.3792106807231903},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3489982485771179},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08667618036270142},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07567766308784485}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8110812902450562},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.8058381676673889},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.7572949528694153},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.7345362901687622},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6656659245491028},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6111290454864502},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.5359009504318237},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5239834189414978},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.46643733978271484},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.46386900544166565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4585523307323456},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.4478800296783447},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4372815787792206},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.43004685640335083},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.395570307970047},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.3792106807231903},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3489982485771179},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08667618036270142},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07567766308784485},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3115/v1/n15-1054","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/n15-1054","pdf_url":"https://aclanthology.org/N15-1054.pdf","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 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.975.8804","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.975.8804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://people.cs.umass.edu/%7Earvind/naacl2015.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.3115/v1/n15-1054","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/n15-1054","pdf_url":"https://aclanthology.org/N15-1054.pdf","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 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5699999928474426,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1699691160.pdf"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W21006490","https://openalex.org/W85083507","https://openalex.org/W205829674","https://openalex.org/W1502473750","https://openalex.org/W1512387364","https://openalex.org/W1532325895","https://openalex.org/W1615249902","https://openalex.org/W1756422141","https://openalex.org/W1852412531","https://openalex.org/W1894439495","https://openalex.org/W2042792636","https://openalex.org/W2085989833","https://openalex.org/W2094728533","https://openalex.org/W2102363952","https://openalex.org/W2107598941","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2128407051","https://openalex.org/W2139694477","https://openalex.org/W2146502635","https://openalex.org/W2146772539","https://openalex.org/W2148721079","https://openalex.org/W2165966284","https://openalex.org/W2187561416","https://openalex.org/W2251673953","https://openalex.org/W2252136820","https://openalex.org/W2406945108","https://openalex.org/W2786972369","https://openalex.org/W3088104242"],"related_works":["https://openalex.org/W842810586","https://openalex.org/W4319940250","https://openalex.org/W2352298027","https://openalex.org/W1541691357","https://openalex.org/W2090135255","https://openalex.org/W2092919065","https://openalex.org/W3138801416","https://openalex.org/W2168409722","https://openalex.org/W4236762297","https://openalex.org/W2782437235"],"abstract_inverted_index":{"Most":[0],"of":[1,14,25,48,118,125,134],"previous":[2],"work":[3],"in":[4,31],"knowledge":[5,63,127],"base":[6,64,128],"(KB)":[7],"completion":[8,65,129],"has":[9],"focused":[10],"on":[11,22,114],"the":[12,23,46,70,94,97,119,123,132],"problem":[13],"relation":[15],"extraction.":[16],"In":[17],"this":[18,49],"work,":[19],"we":[20,51],"focus":[21],"task":[24,36],"inferring":[26],"missing":[27],"entity":[28,95],"type":[29,98],"in-stances":[30],"a":[32,34,53,85,115],"KB,":[33],"fundamental":[35],"for":[37],"KB":[38,72],"competition":[39],"yet":[40],"receives":[41],"little":[42],"attention.":[43],"Due":[44],"to":[45,106,121],"novelty":[47],"task,":[50],"construct":[52],"large-scale":[54],"dataset":[55],"and":[56,73,96,131],"design":[57],"an":[58],"automatic":[59,137],"evaluation":[60,113,138],"methodology.":[61],"Our":[62],"method":[66],"uses":[67],"information":[68,75],"within":[69],"existing":[71],"external":[74],"from":[76,92],"Wikipedia.":[77],"We":[78,109],"show":[79],"that":[80,88],"individual":[81],"methods":[82,130],"trained":[83],"with":[84],"global":[86],"objective":[87],"consid-ers":[89],"unobserved":[90],"cells":[91],"both":[93],"side":[99],"gives":[100],"consistently":[101],"higher":[102],"qual-ity":[103],"predictions":[104],"compared":[105],"baseline":[107],"methods.":[108],"also":[110],"perform":[111],"manual":[112],"small":[116],"subset":[117],"data":[120],"verify":[122],"effectiveness":[124],"our":[126,135],"correctness":[133],"proposed":[136],"method.":[139],"1":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":14},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":6}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}