{"id":"https://openalex.org/W4318148003","doi":"https://doi.org/10.1109/bigdata55660.2022.10021122","title":"Data management in training AI chatbot with personality based on granular computing","display_name":"Data management in training AI chatbot with personality based on granular computing","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318148003","doi":"https://doi.org/10.1109/bigdata55660.2022.10021122"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10021122","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10021122","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/A5009568154","display_name":"Piotr Podolski","orcid":null},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]},{"id":"https://openalex.org/I4654613","display_name":"University of Warsaw","ror":"https://ror.org/039bjqg32","country_code":"PL","type":"education","lineage":["https://openalex.org/I4654613"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Piotr Podolski","raw_affiliation_strings":["University of Warsaw,Faculty of Mathematics, Informatics and Mechanics,Warsaw,Poland","Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"University of Warsaw,Faculty of Mathematics, Informatics and Mechanics,Warsaw,Poland","institution_ids":["https://openalex.org/I4654613","https://openalex.org/I108403487"]},{"raw_affiliation_string":"Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland","institution_ids":["https://openalex.org/I4654613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001085328","display_name":"Tomasz Ludziejewski","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101605","display_name":"Silver Bullet Solutions (United States)","ror":"https://ror.org/00y7m9a70","country_code":"US","type":"company","lineage":["https://openalex.org/I4210101605"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tomasz Ludziejewski","raw_affiliation_strings":["Silver Bullet Solutions,Warsaw,Poland","Silver Bullet Solutions, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Silver Bullet Solutions,Warsaw,Poland","institution_ids":["https://openalex.org/I4210101605"]},{"raw_affiliation_string":"Silver Bullet Solutions, Warsaw, Poland","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044883724","display_name":"Hung Son Nguyen","orcid":"https://orcid.org/0000-0002-3236-5456"},"institutions":[{"id":"https://openalex.org/I4210101605","display_name":"Silver Bullet Solutions (United States)","ror":"https://ror.org/00y7m9a70","country_code":"US","type":"company","lineage":["https://openalex.org/I4210101605"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hung Son Nguyen","raw_affiliation_strings":["Silver Bullet Solutions,Warsaw,Poland","Silver Bullet Solutions, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Silver Bullet Solutions,Warsaw,Poland","institution_ids":["https://openalex.org/I4210101605"]},{"raw_affiliation_string":"Silver Bullet Solutions, Warsaw, Poland","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5009568154"],"corresponding_institution_ids":["https://openalex.org/I108403487","https://openalex.org/I4654613"],"apc_list":null,"apc_paid":null,"fwci":0.518,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6039604,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"6247","last_page":"6252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9984999895095825,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9965000152587891,"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":0.9945999979972839,"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":0.8406919240951538},{"id":"https://openalex.org/keywords/chatbot","display_name":"Chatbot","score":0.697171151638031},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.532272219657898},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.517078161239624},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5151578187942505},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5035516619682312},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.48599109053611755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46505293250083923},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.44517219066619873},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4249241352081299},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.42443645000457764},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3936408758163452},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2809971570968628},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.18794700503349304}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8406919240951538},{"id":"https://openalex.org/C2779041454","wikidata":"https://www.wikidata.org/wiki/Q870780","display_name":"Chatbot","level":2,"score":0.697171151638031},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.532272219657898},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.517078161239624},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5151578187942505},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5035516619682312},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.48599109053611755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46505293250083923},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.44517219066619873},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4249241352081299},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.42443645000457764},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3936408758163452},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2809971570968628},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.18794700503349304},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10021122","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10021122","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":[{"score":0.6299999952316284,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1591706642","https://openalex.org/W1622819908","https://openalex.org/W2086287723","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2153676086","https://openalex.org/W2633911929","https://openalex.org/W2962883855","https://openalex.org/W2988937804","https://openalex.org/W3001279689","https://openalex.org/W3045738072","https://openalex.org/W3085139254","https://openalex.org/W3095645723","https://openalex.org/W3165414374","https://openalex.org/W4239687749","https://openalex.org/W4283455204","https://openalex.org/W4287748555","https://openalex.org/W4288624561","https://openalex.org/W4385245566","https://openalex.org/W6635590879","https://openalex.org/W6679434410","https://openalex.org/W6679436768","https://openalex.org/W6739901393","https://openalex.org/W6758737392","https://openalex.org/W6772383348","https://openalex.org/W6780069040","https://openalex.org/W6784913597","https://openalex.org/W6795997830","https://openalex.org/W6838790558"],"related_works":["https://openalex.org/W3014300295","https://openalex.org/W1629725936","https://openalex.org/W3189515467","https://openalex.org/W2101017737","https://openalex.org/W4361732478","https://openalex.org/W2060192418","https://openalex.org/W2122552724","https://openalex.org/W3201070945","https://openalex.org/W2132388328","https://openalex.org/W1997965068"],"abstract_inverted_index":{"In":[0],"this":[1,24],"article":[2],"we":[3,33,82,109,173],"present":[4,83],"a":[5,15,27,39,99,134,140,148,182],"set":[6],"of":[7,41,52,61,69,105,151,171,178,184,192],"data":[8,101,106,126,163],"handling":[9,167],"techniques":[10,165],"and":[11,44,66,103,116,159,168,180,186],"improvements":[12],"in":[13,31],"training":[14,64,75,108,179],"neural":[16,77],"conversational":[17,76],"model":[18,67,78,132],"on":[19,35,63,124,147],"big":[20],"data.":[21],"We":[22,55,95],"approach":[23,89],"problem":[25,30,40],"as":[26,38],"granular":[28],"computing":[29],"which":[32,144],"focus":[34],"mini-batches":[36],"creation":[37,43,72],"granules":[42],"relations":[45],"between":[46],"them":[47],"to":[48,90,112,121],"optimize":[49],"the":[50,53,93,176,193],"performance":[51,68,191],"model.":[54,194],"also":[56],"provide":[57],"an":[58],"empirical":[59],"summary":[60],"influence":[62],"time":[65,115,177,185],"different":[70],"mini-batch":[71],"strategies":[73],"for":[74,139,166],"with":[79,98,136],"personality.":[80],"Additionally":[81],"our":[84],"own":[85],"uniform":[86],"length":[87],"batching":[88],"speed":[91],"up":[92],"training.":[94],"claim":[96],"that":[97,155],"mindful":[100],"preparation":[102],"use":[104],"during":[107],"are":[110],"able":[111],"reduce":[113,175],"compute":[114,187],"infrastructure":[117],"costs,":[118],"thus":[119],"allowing":[120],"train":[122],"models":[123],"bigger":[125],"at":[127],"reasonable":[128],"time.":[129],"The":[130],"presented":[131],"is":[133],"chat-bot":[135],"topic":[137],"awareness":[138],"mobile":[141],"service":[142],"provider,":[143],"was":[145],"trained":[146],"large":[149],"dataset":[150],"historical":[152],"utterance":[153],"data,":[154,172],"contained":[156],"diverse":[157],"subtopics":[158],"products.":[160],"By":[161],"applying":[162],"optimization":[164],"preparing":[169],"batches":[170],"can":[174],"save":[181],"lot":[183],"costs":[188],"without":[189],"loosing":[190]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}