{"id":"https://openalex.org/W2554653865","doi":"https://doi.org/10.1109/ijcnn.2016.7727658","title":"Scalable focussed entity resolution","display_name":"Scalable focussed entity resolution","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2554653865","doi":"https://doi.org/10.1109/ijcnn.2016.7727658","mag":"2554653865"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727658","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5112608661","display_name":"B N Ranganath","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ranganath B. N.","raw_affiliation_strings":["Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038163398","display_name":"Shalabh Bhatnagar","orcid":"https://orcid.org/0000-0001-7644-3914"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shalabh Bhatnagar","raw_affiliation_strings":["Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112608661"],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07878205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"3570","last_page":"3577"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9991999864578247,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9991999864578247,"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/T10028","display_name":"Topic Modeling","score":0.9980999827384949,"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/T11719","display_name":"Data Quality and Management","score":0.9977999925613403,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8320388793945312},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7252052426338196},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7117874622344971},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5509358048439026},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5353186130523682},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.5265992879867554},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.5081753730773926},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5001091957092285},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.48868706822395325},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4766422212123871},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.4733448326587677},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.46436023712158203},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4434276521205902},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3954113721847534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3922906816005707},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.367313951253891},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.16031122207641602},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08750253915786743}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8320388793945312},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7252052426338196},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7117874622344971},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5509358048439026},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5353186130523682},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.5265992879867554},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.5081753730773926},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5001091957092285},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.48868706822395325},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4766422212123871},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.4733448326587677},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.46436023712158203},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4434276521205902},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3954113721847534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3922906816005707},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.367313951253891},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.16031122207641602},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08750253915786743},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727658","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727658","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W35688032","https://openalex.org/W183759114","https://openalex.org/W1517266559","https://openalex.org/W1529851927","https://openalex.org/W1536860849","https://openalex.org/W1551893515","https://openalex.org/W1559861422","https://openalex.org/W1880262756","https://openalex.org/W1981791873","https://openalex.org/W2002371870","https://openalex.org/W2055341013","https://openalex.org/W2067292826","https://openalex.org/W2069429561","https://openalex.org/W2104676427","https://openalex.org/W2104827998","https://openalex.org/W2112497803","https://openalex.org/W2113227740","https://openalex.org/W2140124448","https://openalex.org/W2151967501","https://openalex.org/W2158266063","https://openalex.org/W2167810193","https://openalex.org/W2168190036","https://openalex.org/W2972891548","https://openalex.org/W4231510805","https://openalex.org/W6607453285","https://openalex.org/W6631033597","https://openalex.org/W6631841336","https://openalex.org/W6632662926","https://openalex.org/W6633432787","https://openalex.org/W6639619044","https://openalex.org/W6675713828","https://openalex.org/W6682569104","https://openalex.org/W6684751948","https://openalex.org/W6767874007"],"related_works":["https://openalex.org/W2888805565","https://openalex.org/W4312773271","https://openalex.org/W4315588616","https://openalex.org/W2769501189","https://openalex.org/W2962686197","https://openalex.org/W2207653751","https://openalex.org/W4293863151","https://openalex.org/W2891616219","https://openalex.org/W2072169887","https://openalex.org/W2352674739"],"abstract_inverted_index":{"The":[0],"problem":[1,32,69],"of":[2,33,44,62,70,96,123,223],"entity":[3,34,71,82,116,224],"resolution":[4,35,72,83,225],"is":[5,15,126,236],"widely":[6],"studied":[7],"in":[8,25,36,73,104,188],"the":[9,13,22,26,31,60,87,91,97,100,105,111,118,124,129,134,156,160,170,201,216,221,241],"research":[10],"community,":[11],"where":[12,39],"goal":[14],"to":[16,213,240],"identify":[17],"real":[18],"users":[19],"associated":[20,119],"with":[21,128],"user":[23],"references":[24],"documents.":[27],"We":[28,173,204],"focus":[29],"on":[30],"dyadic":[37],"data,":[38,75],"associations":[40,51],"between":[41,52,90,113],"one":[42],"pair":[43],"domain":[45],"entities":[46,103,158,230],"such":[47,55],"as":[48,56],"documents-words":[49],"and":[50,99,117,155,162,200,211,235],"another":[53],"pair,":[54],"documents-users":[57],"are":[58],"observed,":[59],"example":[61],"which":[63],"includes":[64],"bibliographic":[65,74,208],"data.":[66],"For":[67],"this":[68],"we":[76,108,141],"propose":[77,142,174],"a":[78,143,175],"Bayesian":[79],"nonparametric":[80],"`Sparse":[81],"model'":[84],"(SERM)":[85],"exploring":[86],"sparse":[88,233],"relationships":[89,234],"grouped":[92],"data":[93],"i.e.,":[94],"grouping":[95],"documents,":[98],"topics,":[101],"author":[102,115,120,157,167,171,229],"group.":[106],"Further,":[107],"also":[109],"exploit":[110],"sparseness":[112],"an":[114],"aliases.":[121],"Grouping":[122],"documents":[125],"achieved":[127],"stick":[130],"breaking":[131],"prior":[132,199],"for":[133,159,165,169,178,196],"Dirichlet":[135],"processes":[136],"(DP).":[137],"To":[138],"achieve":[139],"sparseness,":[140],"solution":[144],"that":[145,215],"introduces":[146],"separate":[147],"Indian":[148],"Buffet":[149],"process":[150],"(IBP)":[151],"priors":[152],"over":[153,207],"topics":[154],"groups":[161],"k-NN":[163,202],"mechanism":[164],"selecting":[166],"aliases":[168],"entities.":[172],"scalable":[176],"inference":[177,193],"SERM":[179,218],"by":[180,226],"appropriately":[181],"combining":[182],"partially":[183],"collapsed":[184],"Gibbs":[185],"sampling":[186],"scheme":[187,194],"Focussed":[189],"topic":[190],"model":[191,219],"(FTM),":[192],"used":[195],"parametric":[197],"IBP":[198],"mechanism.":[203],"perform":[205],"experiments":[206],"datasets,":[209],"Citeseer":[210],"Rexa,":[212],"show":[214],"proposed":[217],"improves":[220],"accuracy":[222],"finding":[227],"relevant":[228],"through":[231],"modeling":[232],"scalable,":[237],"when":[238],"compared":[239],"state-of-the-art":[242],"baseline.":[243]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
