{"id":"https://openalex.org/W3036103472","doi":"https://doi.org/10.1609/aaai.v34i09.7092","title":"Causal Knowledge Extraction through Large-Scale Text Mining","display_name":"Causal Knowledge Extraction through Large-Scale Text Mining","publication_year":2020,"publication_date":"2020-04-03","ids":{"openalex":"https://openalex.org/W3036103472","doi":"https://doi.org/10.1609/aaai.v34i09.7092","mag":"3036103472"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v34i09.7092","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v34i09.7092","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/7092/6946","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/7092/6946","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068065546","display_name":"Oktie Hassanzadeh","orcid":"https://orcid.org/0000-0001-5307-9857"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Oktie Hassanzadeh","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057827968","display_name":"Debarun Bhattacharjya","orcid":"https://orcid.org/0000-0002-9125-1336"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Debarun Bhattacharjya","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031116653","display_name":"Mark Feblowitz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mark Feblowitz","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085594669","display_name":"Kavitha Srinivas","orcid":"https://orcid.org/0000-0003-4610-967X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kavitha Srinivas","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109067952","display_name":"Michael Perrone","orcid":"https://orcid.org/0009-0008-6920-9124"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Perrone","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037217032","display_name":"Shirin Sohrabi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shirin Sohrabi","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004109655","display_name":"Michael Katz","orcid":"https://orcid.org/0000-0001-7445-2286"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Katz","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5068065546"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2135,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.82506573,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"34","issue":"09","first_page":"13610","last_page":"13611"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9761000275611877,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9761000275611877,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9736999869346619,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9726999998092651,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.7187623381614685},{"id":"https://openalex.org/keywords/causal-analysis","display_name":"Causal analysis","score":0.6284543871879578},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.621630847454071},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.5030691027641296},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.45090925693511963},{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.44747868180274963},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.44521364569664},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.4303785562515259},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.41407328844070435},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.39428478479385376},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.388221800327301},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3815656304359436},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3261890709400177},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1626150906085968},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13984593749046326},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1276548206806183},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06535300612449646}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7187623381614685},{"id":"https://openalex.org/C2987525970","wikidata":"https://www.wikidata.org/wiki/Q96374569","display_name":"Causal analysis","level":2,"score":0.6284543871879578},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.621630847454071},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.5030691027641296},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.45090925693511963},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.44747868180274963},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.44521364569664},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.4303785562515259},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.41407328844070435},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.39428478479385376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.388221800327301},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3815656304359436},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3261890709400177},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1626150906085968},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13984593749046326},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1276548206806183},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06535300612449646},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v34i09.7092","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v34i09.7092","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/7092/6946","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v34i09.7092","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v34i09.7092","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/7092/6946","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3036103472.pdf","grobid_xml":"https://content.openalex.org/works/W3036103472.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1811279891","https://openalex.org/W2526352594","https://openalex.org/W2604792945","https://openalex.org/W2842461005","https://openalex.org/W2889363461","https://openalex.org/W2896457183","https://openalex.org/W2963419548","https://openalex.org/W2965536863","https://openalex.org/W3105000654","https://openalex.org/W3145915828","https://openalex.org/W4237403596","https://openalex.org/W6758479639","https://openalex.org/W6786365570","https://openalex.org/W7075292137"],"related_works":["https://openalex.org/W2102962081","https://openalex.org/W2102630578","https://openalex.org/W2623890275","https://openalex.org/W1996752603","https://openalex.org/W1549106357","https://openalex.org/W1709350551","https://openalex.org/W2528529373","https://openalex.org/W1999181696","https://openalex.org/W2082218277","https://openalex.org/W4372338229"],"abstract_inverted_index":{"In":[0],"this":[1],"demonstration,":[2],"we":[3],"present":[4],"a":[5,26,43,50,64,71,81,96,132],"system":[6,24,103],"for":[7,30,39,131],"mining":[8],"causal":[9,31,61,82,97,112],"knowledge":[10],"from":[11,88],"large":[12],"corpuses":[13],"of":[14,20,28,42,49,58,60,66,77,80,95,111],"text":[15],"documents,":[16],"such":[17],"as":[18,53,55],"millions":[19],"news":[21],"articles.":[22],"Our":[23,102],"provides":[25,86],"collection":[27],"APIs":[29,36],"analysis":[32,57,69],"and":[33,46,107,123],"retrieval.":[34],"These":[35],"enable":[37],"searching":[38],"the":[40,47,56,75,78,93],"effects":[41],"given":[44,51,63],"cause":[45],"causes":[48,122],"effect,":[52],"well":[54],"existence":[59,79,94],"relation":[62,98,113],"pair":[65],"phrases.":[67,101],"The":[68],"includes":[70],"score":[72],"that":[73,115],"indicates":[74],"likelihood":[76],"relation.":[83],"It":[84],"also":[85],"evidence":[87],"an":[89],"input":[90,100],"corpus":[91],"supporting":[92],"between":[99],"uses":[104],"generic":[105],"unsupervised":[106],"weakly":[108],"supervised":[109],"methods":[110],"extraction":[114],"do":[116],"not":[117],"impose":[118],"semantic":[119],"constraints":[120],"on":[121],"effects.":[124],"We":[125],"show":[126],"example":[127],"use":[128],"cases":[129],"developed":[130],"commercial":[133],"application":[134],"in":[135],"enterprise":[136],"risk":[137],"management.":[138]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}