{"id":"https://openalex.org/W4320024254","doi":"https://doi.org/10.1109/bigdata55660.2022.10020362","title":"Tab-HGNN: Learning Column Representation with Heterogeneous Graph Neural Network for Web Table Interpretation","display_name":"Tab-HGNN: Learning Column Representation with Heterogeneous Graph Neural Network for Web Table Interpretation","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4320024254","doi":"https://doi.org/10.1109/bigdata55660.2022.10020362"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020362","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020362","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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/A5021349170","display_name":"Xingyu Su","orcid":"https://orcid.org/0000-0001-9764-0968"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xingyu Su","raw_affiliation_strings":["Shanghai Jiao Tong University,Shanghai,China","Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053338416","display_name":"Yanyan Shen","orcid":"https://orcid.org/0000-0001-8364-3674"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Shen","raw_affiliation_strings":["Shanghai Jiao Tong University,Shanghai,China","Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5021349170"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30965293,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"1142","last_page":"1151"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9988999962806702,"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":0.9988999962806702,"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":0.993399977684021,"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":0.9843999743461609,"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/column","display_name":"Column (typography)","score":0.8321021199226379},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6674474477767944},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.62682044506073},{"id":"https://openalex.org/keywords/row-and-column-spaces","display_name":"Row and column spaces","score":0.54740971326828},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5272164344787598},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4978163242340088},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48210835456848145},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4566347897052765},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.45377758145332336},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.4476006031036377},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4349321126937866},{"id":"https://openalex.org/keywords/conceptual-graph","display_name":"Conceptual graph","score":0.419852614402771},{"id":"https://openalex.org/keywords/row","display_name":"Row","score":0.3923463821411133},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3702024817466736},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.3157351613044739},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2949315905570984},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2717747688293457},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.25767678022384644},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.12029373645782471}],"concepts":[{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.8321021199226379},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6674474477767944},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.62682044506073},{"id":"https://openalex.org/C104140500","wikidata":"https://www.wikidata.org/wiki/Q2088159","display_name":"Row and column spaces","level":3,"score":0.54740971326828},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5272164344787598},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4978163242340088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48210835456848145},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4566347897052765},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.45377758145332336},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.4476006031036377},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4349321126937866},{"id":"https://openalex.org/C234837","wikidata":"https://www.wikidata.org/wiki/Q1420493","display_name":"Conceptual graph","level":3,"score":0.419852614402771},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.3923463821411133},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3702024817466736},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.3157351613044739},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2949315905570984},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2717747688293457},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.25767678022384644},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.12029373645782471},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020362","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020362","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1552847225","https://openalex.org/W1880262756","https://openalex.org/W2094728533","https://openalex.org/W2108223890","https://openalex.org/W2245248321","https://openalex.org/W2250539671","https://openalex.org/W2529049456","https://openalex.org/W2602856279","https://openalex.org/W2762307198","https://openalex.org/W2899286282","https://openalex.org/W2907101105","https://openalex.org/W2911286998","https://openalex.org/W2951621897","https://openalex.org/W2952720493","https://openalex.org/W2962902328","https://openalex.org/W2965875055","https://openalex.org/W2998269939","https://openalex.org/W2999905431","https://openalex.org/W3012871709","https://openalex.org/W3013003636","https://openalex.org/W3034617555","https://openalex.org/W3035231859","https://openalex.org/W3082424964","https://openalex.org/W3102264439","https://openalex.org/W3129639992","https://openalex.org/W3155299751","https://openalex.org/W3158303960","https://openalex.org/W3165753548","https://openalex.org/W3168052339","https://openalex.org/W3175971420","https://openalex.org/W4205922070","https://openalex.org/W4221167659","https://openalex.org/W4231449374","https://openalex.org/W4285378361","https://openalex.org/W6629858893","https://openalex.org/W6636510571","https://openalex.org/W6691723933","https://openalex.org/W6726873649","https://openalex.org/W6779462361","https://openalex.org/W6780093097"],"related_works":["https://openalex.org/W2313440505","https://openalex.org/W3123744736","https://openalex.org/W1505848319","https://openalex.org/W2137691148","https://openalex.org/W2905020035","https://openalex.org/W4281399881","https://openalex.org/W3166006430","https://openalex.org/W4281971614","https://openalex.org/W2049180840","https://openalex.org/W3121299875"],"abstract_inverted_index":{"Web":[0],"tables":[1,24],"are":[2,33],"valuable":[3],"resources":[4],"that":[5],"can":[6,69],"enrich":[7],"existing":[8],"knowledge":[9,30],"bases.":[10],"Researchers":[11],"try":[12],"to":[13,89,105,136,178],"make":[14],"the":[15,36,58,63,86,91,107,114,139,157,160,165,172],"predictions":[16],"of":[17,29,75,93,142,159],"columns":[18,94],"and":[19,27,41,52,95,110,144,167,180,186],"column":[20,54,147],"pairs":[21],"in":[22,46,61,97],"web":[23,47,126,153],"into":[25],"entities":[26],"relations":[28],"bases,":[31],"which":[32],"known":[34],"as":[35],"Column":[37],"Type":[38],"Annotation":[39],"(CT)":[40],"Relation":[42],"Extraction":[43],"(RE)":[44],"tasks":[45,112],"table":[48,154],"interpretation.":[49],"Learning":[50],"useful":[51],"accurate":[53],"vector":[55],"representations":[56],"plays":[57],"central":[59],"role":[60],"solving":[62],"two":[64,192],"tasks.":[65,169],"A":[66],"column\u2019s":[67],"semantics":[68,92],"be":[70],"determined":[71],"by":[72,175],"three":[73,140],"kinds":[74,141],"relationships:":[76],"Column-Cell,":[77],"Column-Table,":[78],"Column-Column":[79],"relationships.":[80],"Existing":[81],"works":[82],"only":[83],"rely":[84],"on":[85,151,164,191,207],"Column-Cell":[87],"relationship":[88],"determine":[90],"result":[96],"suboptimal":[98],"performances.":[99],"In":[100],"this":[101],"paper,":[102],"we":[103,121],"propose":[104],"solve":[106],"above":[108],"CT":[109,166,193],"RE":[111,168,209],"with":[113],"heterogeneous":[115,123],"graph":[116],"neural":[117],"network":[118],"technique.":[119],"First,":[120],"construct":[122],"graphs":[124],"for":[125],"tables.":[127],"Next,":[128],"a":[129],"new":[130],"model":[131,163],"named":[132],"Tab-HGNN":[133,162],"is":[134],"proposed":[135,161],"consider":[137],"all":[138],"relationships":[143],"learn":[145],"target":[146],"representations.":[148],"Extensive":[149],"experiments":[150],"real-world":[152],"datasets":[155],"demonstrate":[156],"effectiveness":[158],"It":[170,196],"outperforms":[171],"competitive":[173],"baselines":[174],"achieving":[176],"up":[177],"+7.9%":[179],"+1.6%":[181],"macro":[182,200],"F<inf>1</inf>":[183,189,201,205],"scores,":[184,202],"+2.6%":[185],"+0.2%":[187],"weighted":[188,204],"scores":[190,206],"datasets,":[194],"respectively.":[195],"also":[197],"achieves":[198],"+0.3%":[199],"+0.1%":[203],"one":[208],"dataset.":[210]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
