{"id":"https://openalex.org/W2946845108","doi":"https://doi.org/10.1145/3292500.3330720","title":"Constructing High Precision Knowledge Bases with Subjective and Factual Attributes","display_name":"Constructing High Precision Knowledge Bases with Subjective and Factual Attributes","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2946845108","doi":"https://doi.org/10.1145/3292500.3330720","mag":"2946845108"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330720","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330720","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330720","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330720","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068953995","display_name":"Ari Kobren","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":true,"raw_author_name":"Ari Kobren","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, MA, USA","University of Massachusetts, Amherst"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I24603500"]},{"raw_affiliation_string":"University of Massachusetts, Amherst","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074134679","display_name":"Pablo Barrio","orcid":"https://orcid.org/0000-0002-4410-0682"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pablo Barrio","raw_affiliation_strings":["Google Inc., New York, NY, USA","Google,,,,,"],"affiliations":[{"raw_affiliation_string":"Google Inc., New York, NY, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google,,,,,","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033187873","display_name":"Oksana Yakhnenko","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Oksana Yakhnenko","raw_affiliation_strings":["Google Inc., New York, NY, USA","Google,,,,,"],"affiliations":[{"raw_affiliation_string":"Google Inc., New York, NY, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google,,,,,","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041030715","display_name":"Johann Hibschman","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Johann Hibschman","raw_affiliation_strings":["Google Inc., New York, NY, USA","Google,,,,,"],"affiliations":[{"raw_affiliation_string":"Google Inc., New York, NY, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google,,,,,","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002908234","display_name":"Ian Langmore","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ian Langmore","raw_affiliation_strings":["Google Inc., New York, NY, USA","Google,,,,,"],"affiliations":[{"raw_affiliation_string":"Google Inc., New York, NY, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google,,,,,","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5068953995"],"corresponding_institution_ids":["https://openalex.org/I24603500"],"apc_list":null,"apc_paid":null,"fwci":0.434,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71057672,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2050","last_page":"2058"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9968000054359436,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9968000054359436,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9958000183105469,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9941999912261963,"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/ambiguity","display_name":"Ambiguity","score":0.8300434947013855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7800323367118835},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.6397175788879395},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5903396606445312},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5488702058792114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5399636626243591},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.487722247838974},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.475276380777359},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4668857157230377},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1172846257686615}],"concepts":[{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.8300434947013855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7800323367118835},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6397175788879395},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5903396606445312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5488702058792114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5399636626243591},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.487722247838974},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.475276380777359},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4668857157230377},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1172846257686615},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","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/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3292500.3330720","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330720","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330720","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1905.12807","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.12807","pdf_url":"https://arxiv.org/pdf/1905.12807","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"},{"id":"mag:2946845108","is_oa":true,"landing_page_url":"https://arxiv.org/abs/1905.12807","pdf_url":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1905.12807","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1905.12807","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1145/3292500.3330720","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330720","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330720","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2946845108.pdf","grobid_xml":"https://content.openalex.org/works/W2946845108.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W790941020","https://openalex.org/W1497882404","https://openalex.org/W1512387364","https://openalex.org/W1551839888","https://openalex.org/W1552847225","https://openalex.org/W1563739387","https://openalex.org/W1852412531","https://openalex.org/W1969245231","https://openalex.org/W1977810318","https://openalex.org/W2022166150","https://openalex.org/W2114079787","https://openalex.org/W2123528936","https://openalex.org/W2125261539","https://openalex.org/W2125943921","https://openalex.org/W2129345386","https://openalex.org/W2134305421","https://openalex.org/W2137245235","https://openalex.org/W2140310134","https://openalex.org/W2140890285","https://openalex.org/W2142518823","https://openalex.org/W2146502635","https://openalex.org/W2149273804","https://openalex.org/W2157881433","https://openalex.org/W2168144930","https://openalex.org/W2177901171","https://openalex.org/W2397024933","https://openalex.org/W2475334473","https://openalex.org/W2508504774","https://openalex.org/W2510317721","https://openalex.org/W2575006718","https://openalex.org/W2614919369","https://openalex.org/W2914746235","https://openalex.org/W3102701984","https://openalex.org/W4239943352"],"related_works":["https://openalex.org/W2529654267","https://openalex.org/W100711108","https://openalex.org/W1982310957","https://openalex.org/W3126449980","https://openalex.org/W3009388592","https://openalex.org/W2469545587","https://openalex.org/W2069425874","https://openalex.org/W2597501683","https://openalex.org/W2606460416","https://openalex.org/W30418812","https://openalex.org/W3105967146","https://openalex.org/W89368298","https://openalex.org/W2376793953","https://openalex.org/W2730602818","https://openalex.org/W2253738698","https://openalex.org/W3106127027","https://openalex.org/W3003987449","https://openalex.org/W2141911850","https://openalex.org/W1945823351","https://openalex.org/W2019138604"],"abstract_inverted_index":{"Knowledge":[0],"bases":[1],"(KBs)":[2],"are":[3,11,160],"the":[4,40,112,121,131,170,180,185,210],"backbone":[5],"of":[6,25,145,187,222],"many":[7],"ubiquitous":[8],"applications":[9],"and":[10,79,117,134,147,150,162,199,225,235],"thus":[12,163],"required":[13],"to":[14,65,86,96,119,168,176,232],"exhibit":[15],"high":[16],"precision.":[17,123,172,204],"However,":[18],"for":[19,54,129],"KBs":[20,56,60],"that":[21,61,156,190,227],"store":[22],"subjective":[23,45,77,149],"attributes":[24,78],"entities,":[26],"e.g.,":[27],"whether":[28],"a":[29,52,68,188,196],"movie":[30],"is":[31,37,89,114],"kid":[32],"friendly,":[33],"simply":[34],"estimating":[35],"precision":[36],"complicated":[38],"by":[39,213],"inherent":[41],"ambiguity":[42],"in":[43,111,238],"measuring":[44],"phenomena.":[46],"In":[47],"this":[48],"work,":[49],"we":[50,217],"develop":[51],"method":[53],"constructing":[55],"with":[57,94],"tunable":[58,203],"precision--i.e.,":[59],"can":[62,164],"be":[63,166],"made":[64],"operate":[66],"at":[67],"specific":[69],"false":[70],"positive":[71],"rate,":[72],"despite":[73],"storing":[74],"both":[75],"difficult-to-evaluate":[76],"more":[80],"traditional":[81],"factual":[82,151],"attributes.":[83,152],"The":[84,153],"key":[85],"our":[87,157,207],"approach":[88],"probabilistically":[90],"modeling":[91,102],"user":[92],"consensus":[93,132,182],"respect":[95],"each":[97,103,136,192],"entity-attribute":[98,193],"pair,":[99],"rather":[100],"than":[101],"pair":[104,194],"as":[105,195],"either":[106],"True":[107],"or":[108],"False.":[109],"Uncertainty":[110],"model":[113,133,183,208],"explicitly":[115],"represented":[116],"used":[118,167],"control":[120,169],"KB's":[122,171],"We":[124],"propose":[125],"three":[126],"neural":[127],"networks":[128],"fitting":[130],"evaluate":[135],"one":[137],"on":[138],"data":[139],"from":[140],"Google":[141],"Maps--a":[142],"large":[143],"KB":[144],"locations":[146],"their":[148],"results":[154],"demonstrate":[155],"learned":[158],"models":[159,191],"well-calibrated":[161],"successfully":[165],"Moreover,":[173],"when":[174],"constrained":[175],"maintain":[177],"95%":[178],"precision,":[179],"best":[181],"matches":[184],"F-score":[186],"baseline":[189,212],"binary":[197],"variable":[198],"does":[200],"not":[201],"support":[202],"When":[205],"unconstrained,":[206],"dominates":[209],"same":[211],"12%":[214],"F-score.":[215],"Finally,":[216],"perform":[218],"an":[219],"empirical":[220],"analysis":[221],"attribute-attribute":[223],"correlations":[224],"show":[226],"leveraging":[228],"them":[229],"effectively":[230],"contributes":[231],"reduced":[233],"uncertainty":[234],"better":[236],"performance":[237],"attribute":[239],"prediction.":[240]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}