{"id":"https://openalex.org/W3012566266","doi":"https://doi.org/10.1145/3366423.3380110","title":"Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings","display_name":"Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3012566266","doi":"https://doi.org/10.1145/3366423.3380110","mag":"3012566266"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380110","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380110","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380110","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021158772","display_name":"Yuxuan Shi","orcid":"https://orcid.org/0000-0001-7858-5369"},"institutions":[{"id":"https://openalex.org/I4210151956","display_name":"Robert Bosch (India)","ror":"https://ror.org/04my8ty22","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210151956","https://openalex.org/I889804353"]},{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN","IN"],"is_corresponding":true,"raw_author_name":"Yuxuan Shi","raw_affiliation_strings":["Nanjing University and Bosch Center for AI"],"affiliations":[{"raw_affiliation_string":"Nanjing University and Bosch Center for AI","institution_ids":["https://openalex.org/I881766915","https://openalex.org/I4210151956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063617509","display_name":"Gong Cheng","orcid":"https://orcid.org/0000-0003-3539-7776"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gong Cheng","raw_affiliation_strings":["Nanjing University"],"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025295947","display_name":"Evgeny Kharlamov","orcid":"https://orcid.org/0000-0003-3247-4166"},"institutions":[{"id":"https://openalex.org/I4210151956","display_name":"Robert Bosch (India)","ror":"https://ror.org/04my8ty22","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210151956","https://openalex.org/I889804353"]},{"id":"https://openalex.org/I184942183","display_name":"University of Oslo","ror":"https://ror.org/01xtthb56","country_code":"NO","type":"education","lineage":["https://openalex.org/I184942183"]}],"countries":["IN","NO"],"is_corresponding":false,"raw_author_name":"Evgeny Kharlamov","raw_affiliation_strings":["Bosch Center for AI and University of Oslo"],"affiliations":[{"raw_affiliation_string":"Bosch Center for AI and University of Oslo","institution_ids":["https://openalex.org/I4210151956","https://openalex.org/I184942183"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021158772"],"corresponding_institution_ids":["https://openalex.org/I4210151956","https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":4.0914,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.94707044,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"235","last_page":"245"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9983999729156494,"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"}},{"id":"https://openalex.org/T10720","display_name":"Complexity and Algorithms in Graphs","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/computer-science","display_name":"Computer science","score":0.7528003454208374},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.577654242515564},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5153362154960632},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5082849860191345},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4818178713321686},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4437378942966461},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32771939039230347},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17742931842803955}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7528003454208374},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.577654242515564},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5153362154960632},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5082849860191345},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4818178713321686},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4437378942966461},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32771939039230347},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17742931842803955}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3366423.3380110","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380110","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380110","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380110","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W154362522","https://openalex.org/W1480615301","https://openalex.org/W1527483699","https://openalex.org/W1541239238","https://openalex.org/W1552847225","https://openalex.org/W1572535120","https://openalex.org/W1592186521","https://openalex.org/W1792735297","https://openalex.org/W1972545488","https://openalex.org/W1981585544","https://openalex.org/W2005945380","https://openalex.org/W2008229997","https://openalex.org/W2019003829","https://openalex.org/W2019876129","https://openalex.org/W2031130839","https://openalex.org/W2045446569","https://openalex.org/W2081863845","https://openalex.org/W2096076988","https://openalex.org/W2103260151","https://openalex.org/W2107105629","https://openalex.org/W2112513979","https://openalex.org/W2116391761","https://openalex.org/W2117937654","https://openalex.org/W2130878769","https://openalex.org/W2132534516","https://openalex.org/W2148831787","https://openalex.org/W2159394589","https://openalex.org/W2162833734","https://openalex.org/W2164756925","https://openalex.org/W2169624745","https://openalex.org/W2171570327","https://openalex.org/W2171707538","https://openalex.org/W2171874178","https://openalex.org/W2233447580","https://openalex.org/W2257437291","https://openalex.org/W2437205707","https://openalex.org/W2550610852","https://openalex.org/W2564059478","https://openalex.org/W2592818077","https://openalex.org/W2604863852","https://openalex.org/W2605121203","https://openalex.org/W2621917836","https://openalex.org/W2650213439","https://openalex.org/W2740492458","https://openalex.org/W2782884306","https://openalex.org/W2791658191","https://openalex.org/W2798620495","https://openalex.org/W2900888567","https://openalex.org/W2912798501","https://openalex.org/W2914085604","https://openalex.org/W2914999385","https://openalex.org/W2952005146","https://openalex.org/W2963448850","https://openalex.org/W2964221236"],"related_works":["https://openalex.org/W2370815826","https://openalex.org/W2433057514","https://openalex.org/W2373436826","https://openalex.org/W2167961874","https://openalex.org/W4302065327","https://openalex.org/W2363214567","https://openalex.org/W2104981499","https://openalex.org/W3123242903","https://openalex.org/W2222736632","https://openalex.org/W4309875728"],"abstract_inverted_index":{"Keyword":[0],"search":[1],"is":[2,19],"a":[3,13,51,69,76,79,99,105,114,136],"prominent":[4],"approach":[5,132],"to":[6,89,108,125,134,142],"querying":[7],"Web":[8],"data.":[9],"For":[10,25],"graph-structured":[11],"data,":[12],"widely":[14],"accepted":[15],"semantics":[16],"for":[17],"keywords":[18],"based":[20],"on":[21,39,65,147],"group":[22],"Steiner":[23],"trees.":[24],"this":[26,43],"NP-hard":[27],"problem,":[28],"existing":[29],"algorithms":[30,49,63],"with":[31,50,78],"provable":[32],"quality":[33,53],"guarantees":[34],"have":[35],"prohibitive":[36],"run":[37,60],"time":[38],"large":[40],"graphs.":[41,150],"In":[42],"paper,":[44],"we":[45,87],"propose":[46],"practical":[47],"approximation":[48,139],"guaranteed":[52],"of":[54,81,140],"computed":[55],"answers":[56,141],"and":[57,92,113,120],"very":[58],"low":[59],"time.":[61],"Our":[62,131],"rely":[64],"Hub":[66],"Labeling":[67],"(HL),":[68],"structure":[70],"that":[71,103,118],"labels":[72,124],"each":[73],"vertex":[74,129],"in":[75,145],"graph":[77],"list":[80],"vertices":[82],"reachable":[83],"from":[84],"it,":[85],"which":[86],"use":[88],"compute":[90,135],"distances":[91],"shortest":[93],"paths.":[94],"We":[95],"devise":[96],"two":[97],"HLs:":[98],"conventional":[100],"static":[101,123],"HL":[102,117],"uses":[104],"new":[106],"heuristic":[107],"improve":[109],"pruned":[110],"landmark":[111],"labeling,":[112],"novel":[115],"dynamic":[116],"inverts":[119],"aggregates":[121],"query-relevant":[122],"more":[126],"efficiently":[127],"process":[128],"sets.":[130],"allows":[133],"reasonably":[137],"good":[138],"keyword":[143],"queries":[144],"milliseconds":[146],"million-scale":[148],"knowledge":[149]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}