{"id":"https://openalex.org/W4412877233","doi":"https://doi.org/10.1145/3711896.3737846","title":"RelKD 2025: The Third International Workshop on Resource-Efficient Learning for Knowledge Discovery","display_name":"RelKD 2025: The Third International Workshop on Resource-Efficient Learning for Knowledge Discovery","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412877233","doi":"https://doi.org/10.1145/3711896.3737846"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737846","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737846","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737846","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737846","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022275632","display_name":"Chuxu Zhang","orcid":"https://orcid.org/0000-0002-8349-7926"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chuxu Zhang","raw_affiliation_strings":["University of Connecticut, Storrs, CT, USA"],"affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044455276","display_name":"Kaize Ding","orcid":"https://orcid.org/0000-0001-6684-6752"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaize Ding","raw_affiliation_strings":["Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029588473","display_name":"Jundong Li","orcid":"https://orcid.org/0000-0002-1878-817X"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jundong Li","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068433690","display_name":"Dongkuan Xu","orcid":"https://orcid.org/0000-0002-1456-9658"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongkuan Xu","raw_affiliation_strings":["North Carolina State University, Raleigh, NC, USA"],"affiliations":[{"raw_affiliation_string":"North Carolina State University, Raleigh, NC, USA","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018990800","display_name":"Haoyu Wang","orcid":"https://orcid.org/0000-0001-7485-6213"},"institutions":[{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoyu Wang","raw_affiliation_strings":["University at Albany, Albany, NY, USA"],"affiliations":[{"raw_affiliation_string":"University at Albany, Albany, NY, USA","institution_ids":["https://openalex.org/I392282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055484401","display_name":"Derek Zhiyuan Cheng","orcid":"https://orcid.org/0009-0000-7943-8328"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Derek Zhiyuan Cheng","raw_affiliation_strings":["Google DeepMind, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google DeepMind, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100338946","display_name":"Huan Liu","orcid":"https://orcid.org/0000-0002-3264-7904"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huan Liu","raw_affiliation_strings":["Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5022275632"],"corresponding_institution_ids":["https://openalex.org/I140172145"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09536179,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6308","last_page":"6309"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9563000202178955,"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":0.9563000202178955,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9241999983787537,"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":0.6695965528488159},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.548860490322113},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.5275900959968567},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.5032181143760681},{"id":"https://openalex.org/keywords/knowledge-management","display_name":"Knowledge management","score":0.43690192699432373},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2253648340702057},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.06635144352912903}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6695965528488159},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.548860490322113},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.5275900959968567},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5032181143760681},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.43690192699432373},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2253648340702057},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.06635144352912903}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711896.3737846","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737846","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737846","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711896.3737846","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737846","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737846","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412877233.pdf","grobid_xml":"https://content.openalex.org/works/W4412877233.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W93075631","https://openalex.org/W3005434123","https://openalex.org/W2599205313","https://openalex.org/W2076251662","https://openalex.org/W4231842067"],"abstract_inverted_index":{"Modern":[0],"machine":[1],"learning":[2,101],"techniques,":[3],"particularly":[4],"deep":[5],"learning,":[6],"have":[7],"showcased":[8],"remarkable":[9],"efficacy":[10],"across":[11,74],"numerous":[12],"knowledge":[13],"discovery":[14],"and":[15,50,69,88,139,146],"data":[16,39],"mining":[17],"applications.":[18],"However,":[19],"the":[20,54,105],"advancement":[21],"of":[22,53,78],"these":[23,63],"methods":[24],"is":[25,65,94],"frequently":[26],"impeded":[27],"by":[28,108],"resource":[29,109],"constraint":[30],"challenges":[31,106],"in":[32,45],"many":[33],"scenarios,":[34],"such":[35],"as":[36],"limited":[37],"labeled":[38],"(data-level),":[40],"small":[41],"model":[42],"size":[43],"requirements":[44],"real-world":[46,79],"computing":[47],"platforms":[48],"(model-level),":[49],"efficient":[51,100],"mapping":[52],"computations":[55],"to":[56,98,103,142],"heterogeneous":[57],"target":[58],"hardware":[59],"(system-level).":[60],"Addressing":[61],"all":[62],"factors":[64],"crucial":[66],"for":[67,126,136,149],"effectively":[68],"efficiently":[70],"deploying":[71],"developed":[72],"models":[73],"a":[75,95,133],"broad":[76],"spectrum":[77],"systems,":[80,87],"including":[81],"large-scale":[82],"social":[83],"network":[84],"analysis,":[85],"recommendation":[86],"real-time":[89],"anomaly":[90],"detection.":[91],"Therefore,":[92],"there":[93],"critical":[96],"need":[97],"develop":[99],"techniques":[102],"address":[104],"posed":[107],"limitations,":[110],"whether":[111],"from":[112],"data,":[113],"model/algorithm,":[114],"or":[115],"system/hardware":[116],"perspectives.":[117],"The":[118],"proposed":[119],"third":[120],"international":[121],"workshop":[122],"on":[123],"''Resource-Efficient":[124],"Learning":[125],"Knowledge":[127],"Discovery":[128],"(RelKD":[129],"2025)''":[130],"will":[131],"provide":[132],"great":[134],"venue":[135],"academic":[137],"researchers":[138],"industrial":[140],"practitioners":[141],"share":[143],"challenges,":[144],"solutions,":[145],"future":[147],"opportunities":[148],"resource-efficient":[150],"learning.":[151]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}