{"id":"https://openalex.org/W2529049456","doi":"https://doi.org/10.3233/sw-160242","title":"Effective and efficient Semantic Table Interpretation using TableMiner+","display_name":"Effective and efficient Semantic Table Interpretation using TableMiner+","publication_year":2016,"publication_date":"2016-10-07","ids":{"openalex":"https://openalex.org/W2529049456","doi":"https://doi.org/10.3233/sw-160242","mag":"2529049456"},"language":"en","primary_location":{"id":"doi:10.3233/sw-160242","is_oa":false,"landing_page_url":"https://doi.org/10.3233/sw-160242","pdf_url":null,"source":{"id":"https://openalex.org/S4210177235","display_name":"Semantic Web","issn_l":"1570-0844","issn":["1570-0844","2210-4968"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Semantic Web","raw_type":"journal-article"},"type":"article","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/A5100404392","display_name":"Ziqi Zhang","orcid":"https://orcid.org/0000-0002-8587-8618"},"institutions":[{"id":"https://openalex.org/I52590639","display_name":"Nottingham Trent University","ror":"https://ror.org/04xyxjd90","country_code":"GB","type":"education","lineage":["https://openalex.org/I52590639"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Ziqi Zhang","raw_affiliation_strings":["School of Science and Technology, Nottingham Trent University, 50 Shakespeare Street, Nottingham, NG1\u00a04FQ,\u00a0UK. E-mail:\u00a0ziqi.zhang@ntu.ac.uk"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Technology, Nottingham Trent University, 50 Shakespeare Street, Nottingham, NG1\u00a04FQ,\u00a0UK. E-mail:\u00a0ziqi.zhang@ntu.ac.uk","institution_ids":["https://openalex.org/I52590639"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100404392"],"corresponding_institution_ids":["https://openalex.org/I52590639"],"apc_list":null,"apc_paid":null,"fwci":10.0727,"has_fulltext":false,"cited_by_count":114,"citation_normalized_percentile":{"value":0.98179503,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"8","issue":"6","first_page":"921","last_page":"957"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9969000220298767,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.8205694556236267},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.7528399229049683},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6907082200050354},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5692580938339233},{"id":"https://openalex.org/keywords/bootstrapping","display_name":"Bootstrapping (finance)","score":0.5400773286819458},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.5298654437065125},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5127421617507935},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4796600639820099},{"id":"https://openalex.org/keywords/column","display_name":"Column (typography)","score":0.4705500900745392},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.43371444940567017},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4038352370262146},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36809200048446655},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35618090629577637},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.17756184935569763}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8205694556236267},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.7528399229049683},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6907082200050354},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5692580938339233},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.5400773286819458},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.5298654437065125},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5127421617507935},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4796600639820099},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.4705500900745392},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.43371444940567017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4038352370262146},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36809200048446655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35618090629577637},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.17756184935569763},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.0},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3233/sw-160242","is_oa":false,"landing_page_url":"https://doi.org/10.3233/sw-160242","pdf_url":null,"source":{"id":"https://openalex.org/S4210177235","display_name":"Semantic Web","issn_l":"1570-0844","issn":["1570-0844","2210-4968"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Semantic Web","raw_type":"journal-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:126465","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1132554097","display_name":null,"funder_award_id":"EP/J019488/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W81891695","https://openalex.org/W86887328","https://openalex.org/W92812941","https://openalex.org/W1493270114","https://openalex.org/W1495062271","https://openalex.org/W1501251778","https://openalex.org/W1502473750","https://openalex.org/W1509330973","https://openalex.org/W1524150562","https://openalex.org/W1546430096","https://openalex.org/W1553019137","https://openalex.org/W1561860492","https://openalex.org/W1821396591","https://openalex.org/W1945264926","https://openalex.org/W1996505782","https://openalex.org/W2004087619","https://openalex.org/W2020278455","https://openalex.org/W2034862186","https://openalex.org/W2053238041","https://openalex.org/W2067367118","https://openalex.org/W2068737686","https://openalex.org/W2070491211","https://openalex.org/W2075783691","https://openalex.org/W2084315031","https://openalex.org/W2092364718","https://openalex.org/W2092772700","https://openalex.org/W2108223890","https://openalex.org/W2111869785","https://openalex.org/W2117813082","https://openalex.org/W2138605095","https://openalex.org/W2140602286","https://openalex.org/W2146157311","https://openalex.org/W2153702741","https://openalex.org/W2155273278","https://openalex.org/W2158188757","https://openalex.org/W2159882563","https://openalex.org/W2162020046","https://openalex.org/W2171313960","https://openalex.org/W2229562501","https://openalex.org/W2274241723","https://openalex.org/W2402478263","https://openalex.org/W2588321426","https://openalex.org/W2912132818","https://openalex.org/W6758579119"],"related_works":["https://openalex.org/W1534274833","https://openalex.org/W3117246195","https://openalex.org/W2081850291","https://openalex.org/W2361861616","https://openalex.org/W156620619","https://openalex.org/W2914363205","https://openalex.org/W2616249226","https://openalex.org/W1598221548","https://openalex.org/W3123448197","https://openalex.org/W1510114644"],"abstract_inverted_index":{"This":[0,99],"article":[1],"introduces":[2],"TableMiner+,":[3],"a":[4,14,79,104,137,252,257],"Semantic":[5,132],"Table":[6,133],"Interpretation":[7,134],"method":[8,258],"that":[9,70,108,239],"annotates":[10],"Web":[11],"tables":[12,54],"in":[13,31,84,147,209,229,275,285],"both":[15,50],"effective":[16],"and":[17,52,75,159,166,202,214,278],"efficient":[18],"way.":[19],"Built":[20],"on":[21,112,219,260],"our":[22],"previous":[23],"work":[24],"TableMiner,":[25],"the":[26,85,89,113,118,153,176,194,242,267],"extended":[27],"version":[28],"advances":[29],"state-of-the-art":[30,143,182],"several":[32],"ways.":[33,149],"First,":[34],"it":[35,60,123,206],"improves":[36],"annotation":[37,129,221],"accuracy":[38,277],"by":[39,64,72,103,211],"making":[40],"innova":[41],"tive":[42],"use":[43,177],"of":[44,47,78,96,131,178,231,270,281,283],"various":[45,203],"types":[46],"contextual":[48],"information":[49],"inside":[51],"outside":[53],"as":[55,91,170],"features":[56,248],"for":[57,249],"inference.":[58,250],"Second,":[59],"reduces":[61,226],"computational":[62,227],"overheads":[63,228],"adopting":[65],"an":[66],"incremental,":[67],"bootstrapping":[68],"approach":[69],"starts":[71],"creating":[73],"preliminary":[74],"partial":[76],"annotations":[77],"table":[80,115,204,244],"using":[81,88],"\u2018sample\u2019":[82],"data":[83],"table,":[86],"then":[87,101],"outcome":[90],"\u2018seed\u2019":[92],"to":[93,116,126,157,175,246],"guide":[94],"interpretation":[95],"remaining":[97],"contents.":[98],"is":[100,124],"followed":[102],"message":[105],"passing":[106],"process":[107,241],"iteratively":[109],"refines":[110],"results":[111],"entire":[114,243],"create":[117],"final":[119],"optimal":[120],"annotations.":[121],"Third,":[122],"able":[125],"handle":[127],"all":[128,187,190],"tasks":[130],"(e.g.,":[135],"annotating":[136],"column,":[138],"or":[139],"entity":[140],"cells)":[141],"while":[142],"methods":[144,238],"are":[145,172],"limited":[146],"different":[148,179],"We":[150],"also":[151,224],"compile":[152],"largest":[154],"dataset":[155],"known":[156],"date":[158],"extensively":[160],"evaluate":[161],"TableMiner+":[162,184,271],"against":[163,236,256],"four":[164],"baselines":[165],"two":[167,195],"re-implemented":[168],"(near-identical,":[169],"adaptations":[171],"needed":[173],"due":[174],"knowledge":[180],"bases)":[181],"methods.":[183],"consistently":[185],"outperforms":[186],"models":[188],"under":[189],"experimental":[191],"settings.":[192],"On":[193],"most":[196],"diverse":[197],"datasets":[198],"covering":[199],"multiple":[200],"domains":[201],"schemata,":[205],"achieves":[207,272],"improvement":[208,274],"F1":[210],"between":[212],"1":[213],"42":[215],"percentage":[216],"points":[217],"depending":[218],"specific":[220],"tasks.":[222],"It":[223],"significantly":[225],"terms":[230],"wall-clock":[232,286],"time":[233],"when":[234],"compared":[235,255],"classic":[237],"\u2018exhaustively\u2019":[240],"content":[245],"build":[247],"As":[251],"concrete":[253],"example,":[254],"based":[259],"joint":[261],"inference":[262],"implemented":[263],"with":[264],"parallel":[265],"computation,":[266],"non-parallel":[268],"implementation":[269],"significant":[273],"learning":[276],"almost":[279],"orders":[280],"magnitude":[282],"savings":[284],"time.":[287]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":17},{"year":2019,"cited_by_count":21},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-04T09:04:59.091469","created_date":"2016-10-14T00:00:00"}