{"id":"https://openalex.org/W7147714370","doi":"https://doi.org/10.48550/arxiv.2603.26944","title":"Neuro-Symbolic Learning for Predictive Process Monitoring via Two-Stage Logic Tensor Networks with Rule Pruning","display_name":"Neuro-Symbolic Learning for Predictive Process Monitoring via Two-Stage Logic Tensor Networks with Rule Pruning","publication_year":2026,"publication_date":"2026-03-27","ids":{"openalex":"https://openalex.org/W7147714370","doi":"https://doi.org/10.48550/arxiv.2603.26944"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26944","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26944","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.26944","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073095843","display_name":"Fabrizio De Santis","orcid":"https://orcid.org/0000-0003-3194-826X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"De Santis, Fabrizio","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122901952","display_name":"Gyunam Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Gyunam","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5022402815","display_name":"Francesco Zanichelli","orcid":"https://orcid.org/0000-0002-5802-8343"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zanichelli, Francesco","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073095843"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.24879999458789825,"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":0.24879999458789825,"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":0.12929999828338623,"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/T10703","display_name":"Business Process Modeling and Analysis","score":0.08990000188350677,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6220999956130981},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.5800999999046326},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.48899999260902405},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4546999931335449},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4487000107765198},{"id":"https://openalex.org/keywords/knowledge-acquisition","display_name":"Knowledge acquisition","score":0.40400001406669617},{"id":"https://openalex.org/keywords/axiom","display_name":"Axiom","score":0.3797999918460846}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6500999927520752},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6220999956130981},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.5800999999046326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5253999829292297},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4943000078201294},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.48899999260902405},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4487000107765198},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4165000021457672},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.40400001406669617},{"id":"https://openalex.org/C167729594","wikidata":"https://www.wikidata.org/wiki/Q17736","display_name":"Axiom","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C4777664","wikidata":"https://www.wikidata.org/wiki/Q1536492","display_name":"Linear temporal logic","level":2,"score":0.34529998898506165},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26944","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26944","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.26944","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26944","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.8096945285797119,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Predictive":[0],"modeling":[1],"on":[2,130,134],"sequential":[3,27],"event":[4,33,137],"data":[5,21,117],"is":[6,89],"critical":[7],"for":[8],"fraud":[9],"detection":[10],"and":[11,29,37,48,76,83],"healthcare":[12,42],"monitoring.":[13],"Existing":[14],"data-driven":[15,180],"approaches":[16],"learn":[17],"correlations":[18],"from":[19],"historical":[20],"but":[22],"fail":[23],"to":[24,53,97,115,178,185],"incorporate":[25],"domain-specific":[26],"constraints":[28,67],"logical":[30,66,99],"rules":[31],"governing":[32],"relationships,":[34],"limiting":[35],"accuracy":[36],"regulatory":[38],"compliance.":[39],"For":[40],"example,":[41],"procedures":[43],"must":[44,51],"follow":[45],"specific":[46],"sequences,":[47],"financial":[49],"transactions":[50],"adhere":[52],"compliance":[54],"rules.":[55],"We":[56,72],"present":[57],"a":[58,90],"neuro-symbolic":[59],"approach":[60,108,163],"integrating":[61],"domain":[62,141,186],"knowledge":[63,78,142,154,157],"as":[64],"differentiable":[65],"using":[68,79],"Logic":[69,82],"Networks":[70],"(LTNs).":[71],"formalize":[73],"control-flow,":[74],"temporal,":[75],"payload":[77],"Linear":[80],"Temporal":[81],"first-order":[84],"logic.":[85],"Our":[86],"key":[87],"contribution":[88],"two-stage":[91,150],"optimization":[92,151],"strategy":[93],"addressing":[94],"LTNs'":[95],"tendency":[96],"satisfy":[98],"formulas":[100],"at":[101],"the":[102,149],"expense":[103],"of":[104],"predictive":[105,146],"accuracy.":[106],"The":[107,162],"uses":[109],"weighted":[110],"axiom":[111],"loss":[112],"during":[113],"pretraining":[114],"prioritize":[116],"learning,":[118],"followed":[119],"by":[120],"rule":[121],"pruning":[122],"that":[123,140],"retains":[124],"only":[125],"consistent,":[126],"contributive":[127],"axioms":[128],"based":[129],"satisfaction":[131],"dynamics.":[132],"Evaluation":[133],"four":[135],"real-world":[136],"logs":[138],"shows":[139],"injection":[143],"significantly":[144],"improves":[145],"performance,":[147],"with":[148,169],"proving":[152],"essential":[153],"(without":[155],"it,":[156],"can":[158],"severely":[159],"degrade":[160],"performance).":[161],"excels":[164],"particularly":[165],"in":[166],"compliance-constrained":[167],"scenarios":[168],"limited":[170],"compliant":[171],"training":[172],"examples,":[173],"achieving":[174],"superior":[175],"performance":[176],"compared":[177],"purely":[179],"baselines":[181],"while":[182],"ensuring":[183],"adherence":[184],"constraints.":[187]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}
