{"id":"https://openalex.org/W7136236085","doi":"https://doi.org/10.1109/itsc60802.2025.11423186","title":"A Generalizable Semantic Graph Learning Framework for Road Network Representation","display_name":"A Generalizable Semantic Graph Learning Framework for Road Network Representation","publication_year":2025,"publication_date":"2025-11-18","ids":{"openalex":"https://openalex.org/W7136236085","doi":"https://doi.org/10.1109/itsc60802.2025.11423186"},"language":null,"primary_location":{"id":"doi:10.1109/itsc60802.2025.11423186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc60802.2025.11423186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)","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/A5121213367","display_name":"Yonghui Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yonghui Liu","raw_affiliation_strings":["Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology,Daejeon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology,Daejeon,South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090581700","display_name":"Inhi KIM","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Inhi Kim","raw_affiliation_strings":["Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology,Daejeon,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology,Daejeon,South Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157485424"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72691428,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"720","last_page":"725"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.41110000014305115,"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":0.41110000014305115,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.3626999855041504,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.016899999231100082,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5314000248908997},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43529999256134033},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3327000141143799},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.3301999866962433},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.32760000228881836},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3156999945640564}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6833000183105469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5834000110626221},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5314000248908997},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43529999256134033},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34310001134872437},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33070001006126404},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc60802.2025.11423186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc60802.2025.11423186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W2154851992","https://openalex.org/W2911286998","https://openalex.org/W2962756421","https://openalex.org/W2963512530","https://openalex.org/W3000316997","https://openalex.org/W3016546268","https://openalex.org/W3093035323","https://openalex.org/W3132895077","https://openalex.org/W3210395416","https://openalex.org/W4200081874","https://openalex.org/W4365509842"],"related_works":[],"abstract_inverted_index":{"Road":[0],"networks":[1,136],"(RNs)":[2],"are":[3],"fundamental":[4],"to":[5,29,46,102],"transportation":[6],"systems,":[7],"serving":[8],"as":[9,123],"critical":[10],"input":[11],"for":[12,175],"applications":[13],"like":[14],"traffic":[15],"prediction":[16],"and":[17,35,49,105,115,152],"autonomous":[18],"vehicle":[19],"navigation.":[20],"However,":[21],"effectively":[22],"representing":[23],"large-scale":[24],"RNs":[25],"is":[26],"challenging":[27],"due":[28],"their":[30],"complex":[31,128],"topology,":[32],"dynamic":[33],"evolution,":[34],"heterogeneous":[36,61,172],"associated":[37],"data.":[38],"Existing":[39],"approaches":[40],"often":[41],"struggle":[42],"with":[43,69,171],"scalability,":[44],"generalization":[45,155],"new":[47],"cities,":[48],"integrating":[50],"contextual":[51],"semantic":[52,70,117,169],"information.":[53],"In":[54],"this":[55,87],"work,":[56],"we":[57,89],"propose":[58],"HetSemNet,":[59],"a":[60,80,92,99,147,157],"graph":[62,94,173],"framework":[63],"that":[64,97,138],"enriches":[65],"road":[66,107,113,121,135,148,177],"network":[67,96,178],"graphs":[68],"context":[71],"nodes":[72],"derived":[73],"from":[74],"point-of-interest":[75],"(POI)":[76],"descriptions":[77],"encoded":[78],"by":[79],"pre-trained":[81],"large":[82],"language":[83],"model":[84],"(LLM).":[85],"On":[86],"graph,":[88],"introduce":[90],"SaRNSAGE,":[91],"semantic-aware":[93],"neural":[95],"employs":[98],"sampling-and-aggregation":[100],"mechanism":[101],"learn":[103],"robust":[104],"transferable":[106],"segment":[108],"representations.":[109],"SaRNSAGE":[110],"jointly":[111],"captures":[112],"connectivity":[114],"surrounding":[116],"context,":[118],"leveraging":[119],"basic":[120],"attributes":[122],"initial":[124],"features":[125],"while":[126],"avoiding":[127],"feature":[129],"engineering.":[130],"Experiments":[131],"on":[132,146],"real-world":[133],"urban":[134],"demonstrate":[137],"our":[139],"method":[140],"significantly":[141],"outperforms":[142],"conventional":[143],"GNN":[144],"baselines":[145],"type":[149],"classification":[150],"task":[151],"exhibits":[153],"strong":[154],"in":[156],"cross-city":[158],"transfer":[159],"scenario.":[160],"These":[161],"results":[162],"underscore":[163],"the":[164],"effectiveness":[165],"of":[166],"combining":[167],"LLM-derived":[168],"information":[170],"modeling":[174],"generalizable":[176],"representation":[179],"learning.":[180]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-03-17T00:00:00"}