{"id":"https://openalex.org/W3198043032","doi":"https://doi.org/10.1145/3459637.3482004","title":"GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale","display_name":"GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3198043032","doi":"https://doi.org/10.1145/3459637.3482004","mag":"3198043032"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482004","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2108.13092","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Nicolas Tempelmeier","orcid":null},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Nicolas Tempelmeier","raw_affiliation_strings":["Leibniz Universit\u00e4t Hannover, Hannover, Germany"],"affiliations":[{"raw_affiliation_string":"Leibniz Universit\u00e4t Hannover, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Simon Gottschalk","orcid":null},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Simon Gottschalk","raw_affiliation_strings":["Leibniz Universit\u00e4t Hannover, Hannover, Germany"],"affiliations":[{"raw_affiliation_string":"Leibniz Universit\u00e4t Hannover, Hannover, Germany","institution_ids":["https://openalex.org/I114112103"]}]},{"author_position":"last","author":{"id":null,"display_name":"Elena Demidova","orcid":null},"institutions":[{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Elena Demidova","raw_affiliation_strings":["University of Bonn, Bonn, Germany"],"affiliations":[{"raw_affiliation_string":"University of Bonn, Bonn, Germany","institution_ids":["https://openalex.org/I135140700"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114112103"],"apc_list":null,"apc_paid":null,"fwci":1.1197,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.82480925,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4604","last_page":"4612"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9979000091552734,"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":0.9979000091552734,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9965999722480774,"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":0.9923999905586243,"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/sparql","display_name":"SPARQL","score":0.8118000030517578},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6148999929428101},{"id":"https://openalex.org/keywords/linked-data","display_name":"Linked data","score":0.5985000133514404},{"id":"https://openalex.org/keywords/ontology","display_name":"Ontology","score":0.5867999792098999},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5436999797821045},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.39430001378059387},{"id":"https://openalex.org/keywords/latent-semantic-analysis","display_name":"Latent semantic analysis","score":0.392300009727478},{"id":"https://openalex.org/keywords/semantic-data-model","display_name":"Semantic data model","score":0.3873000144958496},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.38600000739097595}],"concepts":[{"id":"https://openalex.org/C41009113","wikidata":"https://www.wikidata.org/wiki/Q54871","display_name":"SPARQL","level":4,"score":0.8118000030517578},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896000146865845},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6148999929428101},{"id":"https://openalex.org/C69075417","wikidata":"https://www.wikidata.org/wiki/Q515701","display_name":"Linked data","level":3,"score":0.5985000133514404},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.5867999792098999},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5604000091552734},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5436999797821045},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.39430001378059387},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38600000739097595},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3750999867916107},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.36419999599456787},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3472999930381775},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.3271999955177307},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C110903229","wikidata":"https://www.wikidata.org/wiki/Q7449064","display_name":"Semantic integration","level":4,"score":0.3160000145435333},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.29840001463890076},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C2778828372","wikidata":"https://www.wikidata.org/wiki/Q5283209","display_name":"Distributional semantics","level":3,"score":0.28459998965263367},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.2824000120162964},{"id":"https://openalex.org/C57380593","wikidata":"https://www.wikidata.org/wiki/Q933625","display_name":"Volunteered geographic information","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.27730000019073486},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C147497476","wikidata":"https://www.wikidata.org/wiki/Q54872","display_name":"RDF","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3459637.3482004","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2108.13092","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.13092","pdf_url":"https://arxiv.org/pdf/2108.13092","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2108.13092","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.13092","pdf_url":"https://arxiv.org/pdf/2108.13092","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"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4708878974","display_name":null,"funder_award_id":"871477","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1989750313","https://openalex.org/W2080133951","https://openalex.org/W2154851992","https://openalex.org/W2300469216","https://openalex.org/W2534727297","https://openalex.org/W2745627344","https://openalex.org/W2759136286","https://openalex.org/W2768009948","https://openalex.org/W2778839879","https://openalex.org/W2782691731","https://openalex.org/W2945412254","https://openalex.org/W2962739339","https://openalex.org/W2963626623","https://openalex.org/W2980763157","https://openalex.org/W2981644727","https://openalex.org/W2988533489","https://openalex.org/W2990520530","https://openalex.org/W3010977521","https://openalex.org/W3018696946","https://openalex.org/W3023770275","https://openalex.org/W3030252743","https://openalex.org/W3089596632","https://openalex.org/W3096163455","https://openalex.org/W3096751748","https://openalex.org/W3101015102","https://openalex.org/W3107627144","https://openalex.org/W3109484229","https://openalex.org/W3113491032","https://openalex.org/W4292083459"],"related_works":[],"abstract_inverted_index":{"OpenStreetMap":[0],"(OSM)":[1],"is":[2,29],"currently":[3],"the":[4,33,38,74,121,128,153],"richest":[5],"publicly":[6],"available":[7],"information":[8],"source":[9],"on":[10],"geographic":[11,86,97,159],"entities":[12,21,87,101,105,160],"(e.g.,":[13],"buildings":[14],"and":[15,26,44,55,78,96,102,112,130,155],"roads)":[16],"worldwide.":[17],"However,":[18],"using":[19],"OSM":[20,70,76,100],"in":[22,88,161],"machine":[23,109],"learning":[24,110],"models":[25],"other":[27],"applications":[28],"challenging":[30],"due":[31],"to":[32,51,108,127,134,152],"large":[34],"scale":[35],"of":[36,41,47,69,82,99,120,158],"OSM,":[37],"extreme":[39],"heterogeneity":[40],"entity":[42,53,71],"annotations,":[43],"a":[45,48,62,117,141,145],"lack":[46],"well-defined":[49],"ontology":[50],"describe":[52],"semantics":[54],"properties.":[56],"This":[57],"paper":[58],"presents":[59],"GeoVectors":[60,92,122],"-":[61,144],"unique,":[63],"comprehensive":[64],"world-scale":[65],"linked":[66],"open":[67],"corpus":[68,93],"embeddings":[72],"covering":[73],"entire":[75],"dataset":[77],"providing":[79],"latent":[80,156],"representations":[81,157],"over":[83],"980":[84],"million":[85],"180":[89],"countries.":[90],"The":[91],"captures":[94],"semantic":[95,113,118,146,154],"dimensions":[98],"makes":[103],"these":[104],"directly":[106],"accessible":[107],"algorithms":[111],"applications.":[114],"We":[115],"create":[116],"description":[119],"corpus,":[123],"including":[124],"identity":[125],"links":[126],"Wikidata":[129],"DBpedia":[131],"knowledge":[132],"graphs":[133],"supply":[135],"context":[136],"information.":[137],"Furthermore,":[138],"we":[139],"provide":[140],"SPARQL":[142],"endpoint":[143],"interface":[147],"that":[148],"offers":[149],"direct":[150],"access":[151],"OSM.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2021-09-13T00:00:00"}
