{"id":"https://openalex.org/W4416251619","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229419","title":"Towards Robust Neurosymbolic Relational Learning","display_name":"Towards Robust Neurosymbolic Relational Learning","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251619","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229419"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229419","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://openaccess.city.ac.uk/id/eprint/37019/1/_IJCNN_2025____Thais_Sampling.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066858098","display_name":"Thais Luca","orcid":"https://orcid.org/0000-0001-5999-5901"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Thais Luca","raw_affiliation_strings":["COPPE Universidade Federal do Rio de Janeiro,Systems Engineering and Computer Science,Rio de Janeiro,RJ,Brazil"],"affiliations":[{"raw_affiliation_string":"COPPE Universidade Federal do Rio de Janeiro,Systems Engineering and Computer Science,Rio de Janeiro,RJ,Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045549996","display_name":"Aline Paes","orcid":"https://orcid.org/0000-0002-9089-7303"},"institutions":[{"id":"https://openalex.org/I161127581","display_name":"Universidade Federal Fluminense","ror":"https://ror.org/02rjhbb08","country_code":"BR","type":"education","lineage":["https://openalex.org/I161127581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Aline Paes","raw_affiliation_strings":["Universidade Federal Fluminense,Institute of Computing,Niteroi,RJ,Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal Fluminense,Institute of Computing,Niteroi,RJ,Brazil","institution_ids":["https://openalex.org/I161127581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012291023","display_name":"Gerson Zaverucha","orcid":"https://orcid.org/0000-0002-3641-6839"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Gerson Zaverucha","raw_affiliation_strings":["COPPE Universidade Federal do Rio de Janeiro,Systems Engineering and Computer Science,Rio de Janeiro,RJ,Brazil"],"affiliations":[{"raw_affiliation_string":"COPPE Universidade Federal do Rio de Janeiro,Systems Engineering and Computer Science,Rio de Janeiro,RJ,Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060005929","display_name":"Artur d\u2019Avila Garcez","orcid":"https://orcid.org/0000-0001-7375-9518"},"institutions":[{"id":"https://openalex.org/I165862685","display_name":"St George's, University of London","ror":"https://ror.org/040f08y74","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I165862685"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Artur d\u2019Avila Garcez","raw_affiliation_strings":["City St George&#x2019;s, University of London,Dept. of Computer Science,London,UK"],"affiliations":[{"raw_affiliation_string":"City St George&#x2019;s, University of London,Dept. of Computer Science,London,UK","institution_ids":["https://openalex.org/I165862685"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5066858098"],"corresponding_institution_ids":["https://openalex.org/I122140584"],"apc_list":null,"apc_paid":null,"fwci":2.3431,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91807276,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.6227999925613403,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.6227999925613403,"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":0.15929999947547913,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.10209999978542328,"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/statistical-relational-learning","display_name":"Statistical relational learning","score":0.8689000010490417},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.6144999861717224},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4975999891757965},{"id":"https://openalex.org/keywords/inductive-logic-programming","display_name":"Inductive logic programming","score":0.4625000059604645},{"id":"https://openalex.org/keywords/relational-calculus","display_name":"Relational calculus","score":0.40869998931884766},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.38339999318122864},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.36719998717308044},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.3562999963760376},{"id":"https://openalex.org/keywords/top-down-and-bottom-up-design","display_name":"Top-down and bottom-up design","score":0.35589998960494995}],"concepts":[{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.8689000010490417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6922000050544739},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.6144999861717224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6104999780654907},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4975999891757965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47099998593330383},{"id":"https://openalex.org/C2779382394","wikidata":"https://www.wikidata.org/wiki/Q1464197","display_name":"Inductive logic programming","level":2,"score":0.4625000059604645},{"id":"https://openalex.org/C99436015","wikidata":"https://www.wikidata.org/wiki/Q1722436","display_name":"Relational calculus","level":4,"score":0.40869998931884766},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.38339999318122864},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.36719998717308044},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C135798126","wikidata":"https://www.wikidata.org/wiki/Q2167279","display_name":"Top-down and bottom-up design","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C40207289","wikidata":"https://www.wikidata.org/wiki/Q755662","display_name":"Relational model","level":3,"score":0.3384000062942505},{"id":"https://openalex.org/C65647387","wikidata":"https://www.wikidata.org/wiki/Q1781706","display_name":"Conjunctive query","level":3,"score":0.3343999981880188},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.30489999055862427},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2962999939918518},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C49929091","wikidata":"https://www.wikidata.org/wiki/Q1930471","display_name":"General knowledge","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229419","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:openaccess.city.ac.uk:37019","is_oa":true,"landing_page_url":"https://openaccess.city.ac.uk/view/creators_id/a=2Egarcez.html>","pdf_url":"https://openaccess.city.ac.uk/id/eprint/37019/1/_IJCNN_2025____Thais_Sampling.pdf","source":{"id":"https://openalex.org/S4306401940","display_name":"City Research Online (City University London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180825142","host_organization_name":"City, University of London","host_organization_lineage":["https://openalex.org/I180825142"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"}],"best_oa_location":{"id":"pmh:oai:openaccess.city.ac.uk:37019","is_oa":true,"landing_page_url":"https://openaccess.city.ac.uk/view/creators_id/a=2Egarcez.html>","pdf_url":"https://openaccess.city.ac.uk/id/eprint/37019/1/_IJCNN_2025____Thais_Sampling.pdf","source":{"id":"https://openalex.org/S4306401940","display_name":"City Research Online (City University London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180825142","host_organization_name":"City, University of London","host_organization_lineage":["https://openalex.org/I180825142"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G146285273","display_name":null,"funder_award_id":"FAPERJ","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"},{"id":"https://openalex.org/G6111631661","display_name":null,"funder_award_id":"Pesquisa","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"}],"funders":[{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"},{"id":"https://openalex.org/F4320322749","display_name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","ror":"https://ror.org/03kk0s825"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416251619.pdf","grobid_xml":"https://content.openalex.org/works/W4416251619.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1495603628","https://openalex.org/W1512387364","https://openalex.org/W1545331097","https://openalex.org/W1966961673","https://openalex.org/W1976526581","https://openalex.org/W1986767090","https://openalex.org/W1987902506","https://openalex.org/W2021602734","https://openalex.org/W2119831128","https://openalex.org/W2137027418","https://openalex.org/W2141244067","https://openalex.org/W2144429462","https://openalex.org/W2159727905","https://openalex.org/W2164456230","https://openalex.org/W2168658806","https://openalex.org/W3025570773","https://openalex.org/W3037935554","https://openalex.org/W4205198808","https://openalex.org/W4213417280","https://openalex.org/W4236956086","https://openalex.org/W4293249558","https://openalex.org/W4386897562","https://openalex.org/W4390072586","https://openalex.org/W4392964468"],"related_works":[],"abstract_inverted_index":{"Traditional":[0],"neural":[1,24,65,109],"networks":[2,25,110],"(NNs)":[3],"learn":[4],"primarily":[5],"from":[6,64,90,200,242],"data,":[7,35],"which":[8],"limits":[9],"their":[10],"capacity":[11],"to":[12,38,111,128,156,171,188,206,271,283],"represent":[13],"relational":[14,19,34,42,77,113,204,215,244,265],"knowledge":[15,54,78,216],"or":[16,108],"handle":[17],"symbolic":[18,106],"data":[20,201,229],"effectively.":[21],"Although":[22],"graph":[23],"(GNNs)":[26],"address":[27],"this":[28,233],"limitation":[29],"at":[30,40,209,220],"the":[31,57,98,129,152,167,172,183,221,224,249,273,287],"level":[32,212,222],"of":[33,59,131,169,185,213,223,251,289],"they":[36],"continue":[37],"struggle":[39],"learning":[41,45,52,205],"knowledge.":[43],"Neural-symbolic":[44],"offers":[46],"a":[47,72,86,94,158,237],"solution":[48],"by":[49,143,235],"combining":[50],"machine":[51],"with":[53,105,239,263,281],"representation,":[55],"enabling":[56],"development":[58],"interpretable":[60],"logic-based":[61],"models":[62],"learned":[63],"networks.":[66],"Bottom":[67],"clause":[68,84,160,177],"propositionalization":[69],"(BCP)":[70],"is":[71,85,181],"prominent":[73],"approach":[74],"that":[75,134,182,260],"transforms":[76],"into":[79],"attribute-value":[80],"examples.":[81,290],"A":[82],"bottom":[83,159,176,190,198],"logical":[87,132,145],"representation":[88],"created":[89],"each":[91,162,252,255],"example":[92],"as":[93],"starting":[95],"point":[96],"for":[97,161,175,254],"search":[99],"process.":[100],"BCP":[101,116],"can":[102,138,192],"be":[103,193],"used":[104,174],"learners":[107],"tackle":[112],"domains.":[114],"However,":[115,147],"often":[117],"faces":[118],"significant":[119],"memory":[120],"storage":[121,141],"problems":[122,142],"when":[123],"handling":[124],"larger":[125],"datasets":[126],"due":[127],"volume":[130],"literals":[133],"it":[135,148],"generates.":[136],"Semi-propositionalization":[137],"alleviate":[139],"these":[140],"grouping":[144],"literals.":[146],"does":[149],"not":[150],"eliminate":[151],"substantial":[153],"time":[154],"requirements":[155],"create":[157],"example.":[163],"This":[164],"paper":[165],"investigates":[166],"application":[168],"sampling":[170,241,253],"examples":[173,186,266],"generation.":[178],"The":[179,276],"hypothesis":[180,234],"number":[184],"needed":[187],"generate":[189],"clauses":[191,199],"reduced":[194],"significantly.":[195],"Finding":[196],"representative":[197],"should":[202],"enable":[203],"take":[207],"place":[208],"an":[210],"adequate":[211],"abstract":[214],"rather":[217],"than":[218],"simply":[219],"relations":[225],"between":[226],"any":[227],"two":[228],"points.":[230],"We":[231,246],"evaluate":[232],"training":[236,261],"classifier":[238],"different":[240],"five":[243],"datasets.":[245],"experimentally":[247],"validate":[248],"size":[250],"dataset.":[256,275],"Experimental":[257],"results":[258,269,278],"show":[259],"classifiers":[262],"fewer":[264],"produces":[267],"competitive":[268],"compared":[270],"using":[272],"entire":[274],"best":[277],"are":[279],"obtained":[280],"up":[282],"50%":[284],"reduction":[285],"in":[286],"set":[288]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-11-14T00:00:00"}
