{"id":"https://openalex.org/W7167838510","doi":"https://doi.org/10.48550/arxiv.2607.07422","title":"InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs","display_name":"InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs","publication_year":2026,"publication_date":"2026-07-08","ids":{"openalex":"https://openalex.org/W7167838510","doi":"https://doi.org/10.48550/arxiv.2607.07422"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.07422","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07422","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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.2607.07422","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032796119","display_name":"Mayank Kharbanda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kharbanda, Mayank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041317183","display_name":"Michael Cochez","orcid":"https://orcid.org/0000-0001-5726-4638"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cochez, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107464246","display_name":"R Shah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Rajiv Ratn","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027667267","display_name":"Raghava Mutharaju","orcid":"https://orcid.org/0000-0003-2421-3935"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mutharaju, Raghava","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":0.9735000133514404,"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":0.9735000133514404,"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":0.007000000216066837,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.00419999985024333,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/negation","display_name":"Negation","score":0.6845999956130981},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6480000019073486},{"id":"https://openalex.org/keywords/logical-consequence","display_name":"Logical consequence","score":0.5357999801635742},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4927999973297119},{"id":"https://openalex.org/keywords/completeness","display_name":"Completeness (order theory)","score":0.4837000072002411},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4083000123500824},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.40310001373291016},{"id":"https://openalex.org/keywords/conjunctive-query","display_name":"Conjunctive query","score":0.3767000138759613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.725600004196167},{"id":"https://openalex.org/C2185349","wikidata":"https://www.wikidata.org/wiki/Q190558","display_name":"Negation","level":2,"score":0.6845999956130981},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6480000019073486},{"id":"https://openalex.org/C134752490","wikidata":"https://www.wikidata.org/wiki/Q374182","display_name":"Logical consequence","level":2,"score":0.5357999801635742},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4447999894618988},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4020000100135803},{"id":"https://openalex.org/C65647387","wikidata":"https://www.wikidata.org/wiki/Q1781706","display_name":"Conjunctive query","level":3,"score":0.3767000138759613},{"id":"https://openalex.org/C203702819","wikidata":"https://www.wikidata.org/wiki/Q17146953","display_name":"Logical data model","level":3,"score":0.3700999915599823},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.361299991607666},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3228999972343445},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3091000020503998},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C2777502361","wikidata":"https://www.wikidata.org/wiki/Q1182254","display_name":"Deductive database","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2752000093460083},{"id":"https://openalex.org/C136979486","wikidata":"https://www.wikidata.org/wiki/Q773483","display_name":"Existential quantification","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2651999890804291},{"id":"https://openalex.org/C102993220","wikidata":"https://www.wikidata.org/wiki/Q387196","display_name":"Description logic","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C39920170","wikidata":"https://www.wikidata.org/wiki/Q693083","display_name":"Soundness","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.07422","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07422","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.07422","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07422","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Logical":[0],"Multi-Hop":[1],"Query":[2],"Answering":[3],"over":[4,51],"Knowledge":[5],"Graphs":[6],"(KGs)":[7],"can":[8],"be":[9],"formulated":[10],"as":[11,150],"querying,":[12],"with":[13,71,112,132,170],"an":[14],"implicit":[15],"completeness":[16],"assumption.":[17],"Current":[18],"works":[19,40],"mainly":[20],"focus":[21],"on":[22,92,110,146],"Existential":[23],"First":[24],"Order":[25],"Logic":[26],"(EFO)":[27],"queries.":[28],"These":[29,137],"EFO":[30],"queries":[31],"contain":[32],"conjunction,":[33],"disjunction,":[34],"and":[35,65,176],"negation":[36],"operators.":[37],"Most":[38],"existing":[39],"employ":[41],"transductive":[42],"reasoning,":[43],"meaning":[44],"they":[45],"are":[46,181],"not":[47],"capable":[48],"of":[49,75,100,121,127,134,164],"reasoning":[50],"entities":[52],"unseen":[53],"during":[54],"training.":[55],"In":[56],"the":[57,73,96,104,113,119,135,165,171,179],"real":[58],"world,":[59],"there":[60],"is":[61],"a":[62,69,76,83],"resource":[63,139],"scarcity,":[64],"we":[66,80],"cannot":[67],"train":[68],"model":[70,108,155,180],"all":[72,126],"nodes":[74,102],"large":[77,93],"KG.":[78],"Hence,":[79],"propose":[81],"InductWave,":[82],"wavelet-based":[84],"inductive":[85],"embedding":[86],"method":[87],"for":[88,178],"logical":[89],"query":[90],"answering":[91],"KGs.":[94],"Here,":[95],"training":[97],"graph":[98,162],"consists":[99],"fewer":[101,138],"than":[103],"test":[105,153],"graph.":[106],"Our":[107],"performs":[109],"par":[111],"baseline":[114],"models":[115],"while":[116],"having":[117],"half":[118],"number":[120],"message-passing":[122],"layers.":[123,136],"It":[124],"outperforms":[125],"them":[128],"in":[129],"most":[130],"cases,":[131],"75%":[133],"requirements":[140],"enable":[141],"us":[142],"to":[143],"evaluate":[144],"InductWave":[145],"massive":[147],"graphs,":[148],"such":[149],"Wiki-KG.":[151],"We":[152],"our":[154],"using":[156],"extensive":[157],"experiments":[158],"across":[159],"varying":[160],"train-test":[161],"proportions":[163],"FB15k-(237)":[166],"dataset,":[167],"comparing":[168],"it":[169],"state-of-the-art":[172],"models.":[173],"The":[174],"code":[175],"datasets":[177],"available":[182],"at":[183],"https://github.com/kracr/inductwave/.":[184]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-10T00:00:00"}
