{"id":"https://openalex.org/W7162399399","doi":"https://doi.org/10.48550/arxiv.2605.25985","title":"Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables","display_name":"Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162399399","doi":"https://doi.org/10.48550/arxiv.2605.25985"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.25985","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25985","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.25985","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137030670","display_name":"Weizhi Fei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fei, Weizhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137079593","display_name":"Hang Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Hang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137063508","display_name":"Zihao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101060905","display_name":"Shukai Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Shukai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137062138","display_name":"Wei Zhang","orcid":"https://orcid.org/0009-0008-6192-6612"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136999369","display_name":"Yangqiu Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Yangqiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.8364999890327454,"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":0.8364999890327454,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.03099999949336052,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.023800000548362732,"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/ranking","display_name":"Ranking (information retrieval)","score":0.6464999914169312},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6335999965667725},{"id":"https://openalex.org/keywords/tuple","display_name":"Tuple","score":0.5060999989509583},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4812000095844269},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4690999984741211},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.3952000141143799},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.36910000443458557},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.35740000009536743},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.34279999136924744}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7146000266075134},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6464999914169312},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6335999965667725},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.5060999989509583},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4812000095844269},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4690999984741211},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3813000023365021},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.36910000443458557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36070001125335693},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.35740000009536743},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.35580000281333923},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35280001163482666},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.350600004196167},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3310999870300293},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C2778383056","wikidata":"https://www.wikidata.org/wiki/Q873636","display_name":"Namespace","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.25985","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25985","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.25985","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25985","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":"Preprint"},"sustainable_development_goals":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.6786523461341858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Complex":[0],"Query":[1],"Answering":[2,17],"(CQA)":[3],"is":[4,28,190],"a":[5,29,49,74,99,133,149,172],"fundamental":[6],"knowledge":[7,14],"representation":[8],"and":[9,61,130,137],"reasoning":[10],"task":[11],"over":[12,67,148],"incomplete":[13],"graphs":[15],"(KGs).":[16],"existential":[18],"first-order":[19],"queries":[20],"with":[21],"$k$":[22,56],"free":[23,122],"variables":[24,123],"(i.e.,":[25],"$\\text{EFO}_k$":[26,142],"queries)":[27],"crucial":[30],"yet":[31],"challenging":[32],"problem,":[33],"as":[34,55],"it":[35],"requires":[36],"ranking":[37,81,105,162],"answer":[38],"tuples":[39],"in":[40,192],"$\\mathcal{E}^k$,":[41],"where":[42],"$\\mathcal{E}$":[43],"denotes":[44],"the":[45,78],"entity":[46],"set":[47],"of":[48,82,185],"KG.":[50],"This":[51],"quickly":[52],"becomes":[53],"intractable":[54],"grows.":[57],"Consequently,":[58],"existing":[59,177],"benchmarks":[60],"methods":[62],"rely":[63],"on":[64,85],"marginal":[65,71,167],"rankings":[66,72],"individual":[68],"variables;":[69],"however,":[70],"are":[73,128],"poor":[75],"proxy":[76],"for":[77,89],"true":[79],"joint":[80,104,161],"tuples.":[83],"Building":[84],"neural":[86],"symbolic":[87],"search":[88],"$\\text{EFO}_1$":[90,178],"queries,":[91],"we":[92],"propose":[93],"Neural":[94],"Scalable":[95],"Symbolic":[96],"Search":[97],"(NS3),":[98],"budgeted":[100,150],"framework":[101],"that":[102,175],"approximates":[103],"without":[106],"enumerating":[107],"$\\mathcal{E}^k$.":[108],"NS3":[109,158],"(i)":[110],"answers":[111],"marginalized":[112],"sub-queries":[113],"to":[114,144,180],"obtain":[115],"necessary":[116],"candidate":[117],"sets,":[118],"(ii)":[119],"merges":[120],"multiple":[121],"into":[124],"hypernodes":[125],"whose":[126],"domains":[127],"pruned":[129],"controlled":[131],"by":[132],"dynamic":[134],"budget":[135],"$B$,":[136],"(iii)":[138],"progressively":[139],"reduces":[140],"an":[141,145],"query":[143,147],"$\\text{EFO}_{k-1}$":[146],"reduced":[151],"domain.":[152],"Across":[153],"three":[154],"standard":[155],"KG":[156],"datasets,":[157],"substantially":[159],"improves":[160],"performance":[163],"while":[164],"retaining":[165],"strong":[166],"accuracy.":[168],"We":[169],"further":[170],"release":[171],"joint-ranking":[173],"benchmark":[174],"extends":[176],"datasets":[179],"$k=3$,":[181],"enabling":[182],"systematic":[183],"evaluation":[184],"multi-variable":[186],"queries.":[187],"Our":[188],"code":[189],"provided":[191],"https://github.com/HKUST-KnowComp/NS3_KDD2026.":[193]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}