{"id":"https://openalex.org/W3192301111","doi":"https://doi.org/10.24963/ijcai.2021/228","title":"Knowledge-based Residual Learning","display_name":"Knowledge-based Residual Learning","publication_year":2021,"publication_date":"2021-08-01","ids":{"openalex":"https://openalex.org/W3192301111","doi":"https://doi.org/10.24963/ijcai.2021/228","mag":"3192301111"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2021/228","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/228","pdf_url":"https://www.ijcai.org/proceedings/2021/0228.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2021/0228.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5105507434","display_name":"Guanjie Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]},{"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","US"],"is_corresponding":true,"raw_author_name":"Guanjie Zheng","raw_affiliation_strings":["Shanghai Jiao Tong University","The Pennsylvania State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"The Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353236","display_name":"Chang Liu","orcid":"https://orcid.org/0000-0002-3387-0083"},"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":"Chang Liu","raw_affiliation_strings":["Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100777770","display_name":"Hua Wei","orcid":"https://orcid.org/0000-0002-3735-1635"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hua Wei","raw_affiliation_strings":["The Pennsylvania State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060178403","display_name":"Porter Jenkins","orcid":"https://orcid.org/0009-0001-3213-8333"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Porter Jenkins","raw_affiliation_strings":["The Pennsylvania State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028711697","display_name":"Chacha Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chacha Chen","raw_affiliation_strings":["The Pennsylvania State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087213968","display_name":"Tao Wen","orcid":"https://orcid.org/0000-0002-6113-7532"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tao Wen","raw_affiliation_strings":["Syracuse University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016516907","display_name":"Zhenhui Li","orcid":"https://orcid.org/0000-0001-7221-2588"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenhui Li","raw_affiliation_strings":["The Pennsylvania State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5105507434"],"corresponding_institution_ids":["https://openalex.org/I130769515","https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.8792,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.76777545,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1653","last_page":"1659"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9995999932289124,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9846000075340271,"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":0.8101794719696045},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.7242448329925537},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6183896064758301},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5854451656341553},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5455857515335083},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5441071391105652},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4970252811908722},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4804096519947052},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.46121275424957275},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35188114643096924},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1643487513065338},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09570878744125366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8101794719696045},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.7242448329925537},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6183896064758301},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5854451656341553},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5455857515335083},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5441071391105652},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4970252811908722},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4804096519947052},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.46121275424957275},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35188114643096924},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1643487513065338},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09570878744125366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"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:10.24963/ijcai.2021/228","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/228","pdf_url":"https://www.ijcai.org/proceedings/2021/0228.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2021/228","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/228","pdf_url":"https://www.ijcai.org/proceedings/2021/0228.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G4342645261","display_name":"III: Small: Semantic Trajectory Mining with Contexts","funder_award_id":"1618448","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3192301111.pdf","grobid_xml":"https://content.openalex.org/works/W3192301111.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1563938718","https://openalex.org/W1986014385","https://openalex.org/W1988580225","https://openalex.org/W2108738385","https://openalex.org/W2134725820","https://openalex.org/W2139433170","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2245998324","https://openalex.org/W2295598076","https://openalex.org/W2410883106","https://openalex.org/W2418421170","https://openalex.org/W2428977877","https://openalex.org/W2469732900","https://openalex.org/W2538573120","https://openalex.org/W2541674938","https://openalex.org/W2604763608","https://openalex.org/W2734256217","https://openalex.org/W2766291668","https://openalex.org/W2797993462","https://openalex.org/W2948680363","https://openalex.org/W2963377935","https://openalex.org/W2963410064","https://openalex.org/W2964323789","https://openalex.org/W2969053614","https://openalex.org/W2972839598","https://openalex.org/W2982585804","https://openalex.org/W3102476541","https://openalex.org/W3105469151","https://openalex.org/W3163739395","https://openalex.org/W4231274751"],"related_works":["https://openalex.org/W2357854711","https://openalex.org/W4243448361","https://openalex.org/W2051700896","https://openalex.org/W1552255772","https://openalex.org/W2054759342","https://openalex.org/W2111524952","https://openalex.org/W4234690372","https://openalex.org/W4239551281","https://openalex.org/W1583422155","https://openalex.org/W2103484298"],"abstract_inverted_index":{"Small":[0],"data":[1],"has":[2],"been":[3],"a":[4,36,46],"barrier":[5],"for":[6],"many":[7],"machine":[8],"learning":[9],"tasks,":[10],"especially":[11],"when":[12],"applied":[13],"in":[14,31],"scientific":[15],"domains.":[16],"Fortunately,":[17],"we":[18,34],"can":[19,102],"utilize":[20],"domain":[21,42,68,100],"knowledge":[22,43,69,101],"to":[23,55,64],"make":[24],"up":[25],"the":[26,84,99,104],"lack":[27],"of":[28,87],"data.":[29],"Hence,":[30],"this":[32],"paper,":[33],"propose":[35],"hybrid":[37],"model":[38,44,54,70,75],"KRL":[39,61,88],"that":[40,60],"treats":[41],"as":[45],"weak":[47],"learner":[48],"and":[49,71],"uses":[50],"another":[51],"neural":[52,73],"net":[53,74],"boost":[56],"it.":[57],"We":[58],"prove":[59],"is":[62],"guaranteed":[63],"improve":[65],"over":[66,89],"pure":[67,72],"under":[76],"certain":[77],"loss":[78],"functions.":[79],"Extensive":[80],"experiments":[81],"have":[82,96],"shown":[83],"superior":[85],"performance":[86],"baselines.":[90],"In":[91],"addition,":[92],"several":[93],"case":[94],"studies":[95],"explained":[97],"how":[98],"assist":[103],"prediction.":[105]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}