{"id":"https://openalex.org/W4414359633","doi":"https://doi.org/10.24963/ijcai.2025/1049","title":"HygMap: Representing All Types of Map Entities via Heterogeneous Hypergraph","display_name":"HygMap: Representing All Types of Map Entities via Heterogeneous Hypergraph","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414359633","doi":"https://doi.org/10.24963/ijcai.2025/1049"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/1049","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/1049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","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/A5032964730","display_name":"Yifan Yang","orcid":"https://orcid.org/0000-0001-9127-168X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Yang","raw_affiliation_strings":["Beihang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100613279","display_name":"Jingyuan Wang","orcid":"https://orcid.org/0000-0002-4267-4922"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyuan Wang","raw_affiliation_strings":["Beihang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104093622","display_name":"Xie Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xie Yu","raw_affiliation_strings":["Beihang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yibang Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibang Tang","raw_affiliation_strings":["Beihang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82880672"],"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":"9438","last_page":"9446"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9977999925613403,"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":0.9977999925613403,"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/T10757","display_name":"Geographic Information Systems Studies","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9933000206947327,"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/component","display_name":"Component (thermodynamics)","score":0.5990999937057495},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5170999765396118},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5116000175476074},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.41260001063346863},{"id":"https://openalex.org/keywords/semantic-mapping","display_name":"Semantic mapping","score":0.40540000796318054},{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.36169999837875366},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.34769999980926514},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.3346000015735626},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.32100000977516174}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7680000066757202},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5990999937057495},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5170999765396118},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5116000175476074},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4819999933242798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42309999465942383},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.41260001063346863},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C2780366209","wikidata":"https://www.wikidata.org/wiki/Q5170200","display_name":"Core model","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.296999990940094},{"id":"https://openalex.org/C144745244","wikidata":"https://www.wikidata.org/wiki/Q4927286","display_name":"Blocking (statistics)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2777000069618225},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C188048851","wikidata":"https://www.wikidata.org/wiki/Q2298569","display_name":"Road map","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/1049","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/1049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Maps":[0],"are":[1],"crucial":[2],"for":[3,29,74,127],"various":[4],"smart":[5],"city":[6,13],"applications":[7],"as":[8,120],"a":[9,63,90,106,121,132],"core":[10],"component":[11],"of":[12,56],"geographic":[14],"information":[15,28],"systems":[16],"(GIS).":[17],"Developing":[18],"effective":[19],"Map":[20],"Entity":[21],"Representation":[22],"Learning":[23],"methods":[24,60],"can":[25],"extract":[26],"semantic":[27],"downstream":[30,153],"tasks":[31,154],"like":[32],"crime":[33],"rate":[34],"prediction":[35],"and":[36,54,70,80,101,130,170],"land":[37,50],"use":[38],"classification,":[39],"with":[40,155,166],"significant":[41],"application":[42],"potential.":[43],"A":[44],"map":[45,83,97,113,119,128,145],"comprises":[46],"three":[47],"entity":[48,65,84,114],"types:":[49],"parcels,":[51],"road":[52],"segments,":[53],"points":[55],"interest.":[57],"Most":[58],"existing":[59],"focus":[61],"on":[62,151],"single":[64],"type,":[66],"losing":[67],"inter-entity":[68],"relationships":[69,143],"weakening":[71],"representation":[72],"effectiveness":[73],"real-world":[75,157],"applications.":[76],"Thus,":[77],"jointly":[78],"modelling":[79],"representing":[81],"multiple":[82],"types":[85],"is":[86,93],"essential.":[87],"However,":[88],"designing":[89],"unified":[91],"framework":[92,162],"challenging":[94],"due":[95],"to":[96,110],"data's":[98],"unstructured,":[99],"complex,":[100],"heterogeneous":[102,122,142],"nature.":[103],"We":[104,116],"propose":[105],"novel":[107],"method,":[108],"HygMap,":[109],"represent":[111],"all":[112,164],"types.":[115],"model":[117],"the":[118,141],"hypergraph,":[123],"design":[124],"an":[125],"encoder":[126],"entities,":[129],"introduce":[131],"hybrid":[133],"self-supervised":[134],"training":[135],"scheme.":[136],"This":[137],"architecture":[138],"comprehensively":[139],"captures":[140],"among":[144],"entities":[146],"at":[147],"different":[148],"levels.":[149],"Experiments":[150],"nine":[152],"two":[156],"datasets":[158],"show":[159],"that":[160],"our":[161],"outperforms":[163],"baselines,":[165],"good":[167],"computational":[168],"efficiency":[169],"scalability.":[171]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
