{"id":"https://openalex.org/W2977248041","doi":"https://doi.org/10.1145/3360901.3364432","title":"Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs","display_name":"Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs","publication_year":2019,"publication_date":"2019-09-23","ids":{"openalex":"https://openalex.org/W2977248041","doi":"https://doi.org/10.1145/3360901.3364432","mag":"2977248041"},"language":"en","primary_location":{"id":"doi:10.1145/3360901.3364432","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3360901.3364432","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3360901.3364432","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Knowledge Capture","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3360901.3364432","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gengchen Mai","orcid":null},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gengchen Mai","raw_affiliation_strings":["University of California, Santa Barbara, Santa Barbara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Krzysztof Janowicz","orcid":null},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Krzysztof Janowicz","raw_affiliation_strings":["University of California, Santa Barbara, Santa Barbara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bo Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bo Yan","raw_affiliation_strings":["University of California, Santa Barbara, Santa Barbara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Rui Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Zhu","raw_affiliation_strings":["University of California, Santa Barbara, Santa Barbara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ling Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ling Cai","raw_affiliation_strings":["University of California, Santa Barbara, Santa Barbara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Santa Barbara, Santa Barbara, CA, USA","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ni Lao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni Lao","raw_affiliation_strings":["SayMosaic Inc., Palo Alto, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SayMosaic Inc., Palo Alto, CA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"171","last_page":"178"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9993000030517578,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9926999807357788,"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/leverage","display_name":"Leverage (statistics)","score":0.6147000193595886},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.613099992275238},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.558899998664856},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5473999977111816},{"id":"https://openalex.org/keywords/logical-consequence","display_name":"Logical consequence","score":0.40959998965263367},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.39959999918937683},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.35359999537467957},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.35010001063346863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7236999869346619},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6147000193595886},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.613099992275238},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.558899998664856},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5473999977111816},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4196000099182129},{"id":"https://openalex.org/C134752490","wikidata":"https://www.wikidata.org/wiki/Q374182","display_name":"Logical consequence","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.39959999918937683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39899998903274536},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C24755975","wikidata":"https://www.wikidata.org/wiki/Q4943354","display_name":"Boolean conjunctive query","level":5,"score":0.34540000557899475},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.290800005197525},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28040000796318054},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2727999985218048},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.266400009393692},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.2535000145435333},{"id":"https://openalex.org/C203702819","wikidata":"https://www.wikidata.org/wiki/Q17146953","display_name":"Logical data model","level":3,"score":0.25279998779296875},{"id":"https://openalex.org/C118689300","wikidata":"https://www.wikidata.org/wiki/Q7978614","display_name":"Web query classification","level":4,"score":0.25040000677108765}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3360901.3364432","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3360901.3364432","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3360901.3364432","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Knowledge Capture","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1910.00084","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.00084","pdf_url":"https://arxiv.org/pdf/1910.00084","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:research-information.bris.ac.uk:openaire_cris_publications/4f789cfd-bfc4-449a-938b-5717a697f72f","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/4f789cfd-bfc4-449a-938b-5717a697f72f","pdf_url":"https://research-information.bris.ac.uk/files/346260339/1910.00084v1.pdf","source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Mai, G, Janowicz, K, Yan, B, Zhu, R, Cai, L & Lao, N 2019, Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs. in K-CAP 2019 - Proceedings of the 10th International Conference on Knowledge Capture. K-CAP, Association for Computing Machinery, pp. 171-178. https://doi.org/10.1145/3360901.3364432, https://doi.org/10.1145/3360901.3364432","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"doi:10.1145/3360901.3364432","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3360901.3364432","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3360901.3364432","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Knowledge Capture","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2977248041.pdf","grobid_xml":"https://content.openalex.org/works/W2977248041.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W1529533208","https://openalex.org/W2099752825","https://openalex.org/W2132537081","https://openalex.org/W2184957013","https://openalex.org/W2194775991","https://openalex.org/W2250342289","https://openalex.org/W2546950329","https://openalex.org/W2604314403","https://openalex.org/W2759136286","https://openalex.org/W2949972983"],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"several":[1],"studies":[2],"have":[3],"explored":[4],"methods":[5],"for":[6],"using":[7],"KG":[8,127],"embedding":[9,18,68],"to":[10,30,43,49,82,112,152,165],"answer":[11],"logical":[12,95],"queries.":[13],"These":[14],"approaches":[15],"either":[16],"treat":[17],"learning":[19,26],"and":[20,117,143,156,162],"query":[21,39,56,96],"answering":[22,97],"as":[23,129,131,192,194],"two":[24,139],"separated":[25],"tasks,":[27],"or":[28],"fail":[29],"deal":[31],"with":[32,180],"the":[33,51,65,78,114,118,125,132,153,167,170,177,186],"variability":[34],"of":[35,54,169],"contributions":[36],"from":[37],"different":[38,55],"paths.":[40,57],"We":[41,136],"proposed":[42,171,178],"leverage":[44],"a":[45,91],"graph":[46,61],"attention":[47,62],"mechanism":[48],"handle":[50],"unequal":[52],"contribution":[53],"However,":[58],"commonly":[59],"used":[60],"assumes":[63],"that":[64,176],"center":[66,79,115],"node":[67,80],"is":[69,72,81,121],"provided,":[70],"which":[71,105,145],"unavailable":[73],"in":[74,149],"this":[75,87],"task":[76],"since":[77],"be":[83],"predicted.":[84],"To":[85],"solve":[86],"problem":[88],"we":[89],"propose":[90],"multi-head":[92],"attention-based":[93],"end-to-end":[94],"model,":[98],"called":[99],"Contextual":[100],"Graph":[101],"Attention":[102],"model":[103,120],"(CGA),":[104],"uses":[106],"an":[107],"initial":[108],"neighborhood":[109],"aggregation":[110],"layer":[111],"generate":[113],"embedding,":[116],"whole":[119],"trained":[122],"jointly":[123],"on":[124,189],"original":[126],"structure":[128],"well":[130,193],"sampled":[133],"query-answer":[134],"pairs.":[135],"also":[137],"introduce":[138],"new":[140],"datasets,":[141],"DB18":[142],"WikiGeo19,":[144],"are":[146],"rather":[147],"large":[148],"size":[150],"compared":[151],"existing":[154],"datasets":[155,191],"contain":[157],"many":[158],"more":[159],"relation":[160],"types,":[161],"use":[163],"them":[164],"evaluate":[166],"performance":[168],"model.":[172],"Our":[173],"result":[174],"shows":[175],"CGA":[179],"fewer":[181],"learnable":[182],"parameters":[183],"consistently":[184],"outperforms":[185],"baseline":[187],"models":[188],"both":[190],"Bio":[195],"dataset.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2019-10-10T00:00:00"}
