{"id":"https://openalex.org/W2340448811","doi":"https://doi.org/10.1145/2911451.2911509","title":"Hierarchical Random Walk Inference in Knowledge Graphs","display_name":"Hierarchical Random Walk Inference in Knowledge Graphs","publication_year":2016,"publication_date":"2016-07-07","ids":{"openalex":"https://openalex.org/W2340448811","doi":"https://doi.org/10.1145/2911451.2911509","mag":"2340448811"},"language":"en","primary_location":{"id":"doi:10.1145/2911451.2911509","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2911451.2911509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077109703","display_name":"Qiao Liu","orcid":"https://orcid.org/0000-0002-2573-9544"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiao Liu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039760665","display_name":"Liuyi Jiang","orcid":"https://orcid.org/0000-0002-9377-2709"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liuyi Jiang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010948961","display_name":"Han Minghao","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minghao Han","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100742036","display_name":"Yao Liu","orcid":"https://orcid.org/0000-0001-5342-9896"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Liu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045032131","display_name":"Zhiguang Qin","orcid":"https://orcid.org/0000-0001-6745-6377"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiguang Qin","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5077109703"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":3.9874,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.94332543,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"445","last_page":"454"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991999864578247,"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":0.9991999864578247,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9991999864578247,"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":0.9889000058174133,"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/inference","display_name":"Inference","score":0.8732620477676392},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6704275012016296},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.619999885559082},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.6051217317581177},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.5897039175033569},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.5844252109527588},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.579207718372345},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.4849384129047394},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.43830031156539917},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43207111954689026},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.41798123717308044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.397786945104599},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37227851152420044},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31261956691741943},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26236245036125183}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8732620477676392},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6704275012016296},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.619999885559082},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.6051217317581177},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.5897039175033569},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.5844252109527588},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.579207718372345},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.4849384129047394},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.43830031156539917},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43207111954689026},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.41798123717308044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.397786945104599},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37227851152420044},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31261956691741943},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26236245036125183},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2911451.2911509","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2911451.2911509","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7225055007","display_name":null,"funder_award_id":"61133016,61502087,U1401257","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W175897666","https://openalex.org/W205829674","https://openalex.org/W1529533208","https://openalex.org/W1543747524","https://openalex.org/W1756422141","https://openalex.org/W1860880244","https://openalex.org/W1971838979","https://openalex.org/W1973616272","https://openalex.org/W1977970897","https://openalex.org/W2002826893","https://openalex.org/W2029249040","https://openalex.org/W2091125641","https://openalex.org/W2092045293","https://openalex.org/W2099752825","https://openalex.org/W2127795553","https://openalex.org/W2150475393","https://openalex.org/W2184957013","https://openalex.org/W2247119764","https://openalex.org/W2283196293","https://openalex.org/W2584683887","https://openalex.org/W3126976873"],"related_works":["https://openalex.org/W3181676408","https://openalex.org/W1549959306","https://openalex.org/W298858370","https://openalex.org/W320292658","https://openalex.org/W2212764924","https://openalex.org/W2186138942","https://openalex.org/W2806326686","https://openalex.org/W4386298164","https://openalex.org/W2001007279","https://openalex.org/W2079674650"],"abstract_inverted_index":{"Relational":[0],"inference":[1,18,72,82,108,185],"is":[2,19,135,144],"a":[3,79,130],"crucial":[4],"technique":[5],"for":[6,84],"knowledge":[7,32,91,171,203],"base":[8],"population.":[9],"The":[10,113,187],"central":[11],"problem":[12],"in":[13,30,87,122,170],"the":[14,27,31,48,53,97,101,138,148,154,163,167,178,181,206],"study":[15],"of":[16,59,100,153,166,180],"relational":[17,85,184],"to":[20,42,176,210],"infer":[21],"unknown":[22],"relations":[23,155],"between":[24,132,147],"entities":[25,134],"from":[26,116,201],"facts":[28],"given":[29],"bases.":[33],"Two":[34],"popular":[35],"models":[36,51],"have":[37],"been":[38],"put":[39],"forth":[40],"recently":[41],"solve":[43],"this":[44,75,123,142],"problem,":[45],"which":[46,93],"are":[47,156,192,208],"latent":[49],"factor":[50],"and":[52,64,71,190,205],"random-walk":[54,81,102,182],"models,":[55,103],"respectively.":[56],"However,":[57],"each":[58],"them":[60],"has":[61],"their":[62,68],"pros":[63],"cons,":[65],"depending":[66],"on":[67,197],"computational":[69,98],"efficiency":[70],"accuracy.":[73],"In":[74],"paper,":[76],"we":[77,120,126,160],"propose":[78],"hierarchical":[80],"algorithm":[83,189],"learning":[86],"large":[88],"scale":[89],"graph-structured":[90],"bases,":[92,204],"not":[94],"only":[95],"maintains":[96],"simplicity":[99],"but":[104],"also":[105],"provides":[106],"better":[107],"accuracy":[109],"than":[110],"related":[111],"works.":[112],"improvements":[114],"come":[115],"two":[117,133],"basic":[118],"assumptions":[119],"proposed":[121,188],"paper.":[124],"Firstly,":[125],"assume":[127,161],"that":[128,162],"although":[129],"relation":[131,143],"syntactically":[136],"directional,":[137],"information":[139],"conveyed":[140],"by":[141],"equally":[145],"shared":[146],"connected":[149],"entities,":[150],"thus":[151],"all":[152],"semantically":[157],"bidirectional.":[158],"Secondly,":[159],"topology":[164],"structures":[165],"relation-specific":[168],"subgraphs":[169],"bases":[172],"can":[173],"be":[174],"exploited":[175],"improve":[177],"performance":[179],"based":[183],"algorithms.":[186],"ideas":[191],"validated":[193],"with":[194],"numerical":[195],"results":[196,207],"experimental":[198],"data":[199],"sampled":[200],"practical":[202],"compared":[209],"state-of-the-art":[211],"approaches.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2025-10-10T00:00:00"}