{"id":"https://openalex.org/W4408354661","doi":"https://doi.org/10.1109/icassp49660.2025.10890317","title":"Meta-Analogy Learning Based on Dynamic Graph Neural Networks for Inductive Knowledge Graph Link Prediction","display_name":"Meta-Analogy Learning Based on Dynamic Graph Neural Networks for Inductive Knowledge Graph Link Prediction","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354661","doi":"https://doi.org/10.1109/icassp49660.2025.10890317"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10890317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890317","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100432472","display_name":"Jingyu Wang","orcid":"https://orcid.org/0000-0002-8056-2251"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyu Wang","raw_affiliation_strings":["Inner Mongolia University,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101586033","display_name":"Zhijuan Du","orcid":"https://orcid.org/0000-0002-0502-8374"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijuan Du","raw_affiliation_strings":["Inner Mongolia University,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101492561","display_name":"Tao Sun","orcid":"https://orcid.org/0000-0003-2609-2153"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Sun","raw_affiliation_strings":["Inner Mongolia University,Hohhot,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,Hohhot,China","institution_ids":["https://openalex.org/I2722730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2722730"],"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":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13832","display_name":"Advanced Decision-Making Techniques","score":0.9731000065803528,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T13832","display_name":"Advanced Decision-Making Techniques","score":0.9731000065803528,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7245479822158813},{"id":"https://openalex.org/keywords/analogy","display_name":"Analogy","score":0.6829659938812256},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.6106539368629456},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.564948558807373},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46240952610969543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4330725073814392},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3995199203491211},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3393076956272125}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7245479822158813},{"id":"https://openalex.org/C521332185","wikidata":"https://www.wikidata.org/wiki/Q185816","display_name":"Analogy","level":2,"score":0.6829659938812256},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.6106539368629456},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.564948558807373},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46240952610969543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4330725073814392},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3995199203491211},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3393076956272125},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10890317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890317","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321408","display_name":"Ministry of Education","ror":"https://ror.org/01p262204"},{"id":"https://openalex.org/F4320322868","display_name":"Natural Science Foundation of Inner Mongolia","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2151502664","https://openalex.org/W2250635077","https://openalex.org/W2604314403","https://openalex.org/W2759136286","https://openalex.org/W2962886429","https://openalex.org/W2963571857","https://openalex.org/W2971167006","https://openalex.org/W2997897037","https://openalex.org/W3003265726","https://openalex.org/W3021393704","https://openalex.org/W3113170987","https://openalex.org/W3174905206","https://openalex.org/W4226253567","https://openalex.org/W4284687473","https://openalex.org/W4285605599","https://openalex.org/W4306386173","https://openalex.org/W4372260528","https://openalex.org/W4375850653","https://openalex.org/W4382239759","https://openalex.org/W4385270435","https://openalex.org/W4392903384","https://openalex.org/W4392904103","https://openalex.org/W4396706505","https://openalex.org/W4401972203","https://openalex.org/W4403792035","https://openalex.org/W6678830454","https://openalex.org/W6758075616","https://openalex.org/W6767364878","https://openalex.org/W6769589523"],"related_works":["https://openalex.org/W2392206215","https://openalex.org/W2365201483","https://openalex.org/W2355561779","https://openalex.org/W2352407775","https://openalex.org/W108701362","https://openalex.org/W2186567693","https://openalex.org/W2469799552","https://openalex.org/W2768582344","https://openalex.org/W28964973","https://openalex.org/W2615704157"],"abstract_inverted_index":{"For":[0],"inductive":[1],"link":[2],"prediction":[3],"in":[4,57],"knowledge":[5,59],"graphs,":[6],"we":[7,85],"address":[8,82],"the":[9,24,33,43,116,149],"problem":[10,49],"by":[11],"considering":[12],"bridging":[13],"links":[14,16],"(i.e.,":[15],"connecting":[17],"discrete":[18,51],"graphs).":[19],"Although":[20],"current":[21],"research":[22],"overcomes":[23],"traditional":[25],"graph":[26,98,106],"topological":[27],"constraints,":[28],"it":[29,62],"tends":[30],"to":[31,67,75,126,137,147,152,154],"ignore":[32],"dynamic":[34,97,105],"interactions":[35,110],"between":[36,79],"relations":[37],"and":[38,52,71,74,111,123,146],"entities":[39],"as":[40,42],"well":[41],"capture":[44,127],"of":[45,50,55,144],"global":[46,70,113],"information.":[47],"The":[48,104],"small":[53,142],"amounts":[54,143],"information":[56,73,125],"real-world":[58],"graphs":[60],"makes":[61],"difficult":[63],"for":[64],"existing":[65,164],"methods":[66],"effectively":[68],"integrate":[69],"local":[72,128],"model":[76],"complex":[77],"relationships":[78],"entities.":[80],"To":[81],"these":[83],"issues,":[84],"propose":[86],"a":[87],"novel":[88],"meta-analogy":[89],"learning":[90],"framework,":[91],"Ank-motor,":[92],"which":[93],"integrates":[94,120],"autonomously":[95],"designed":[96],"neural":[99,107],"networks":[100],"with":[101,139,141],"analogical":[102,117],"reasoning.":[103],"network":[108],"models":[109,165],"captures":[112],"information,":[114],"while":[115],"inference":[118],"layer":[119],"entity,":[121],"relation,":[122],"triple-layer":[124],"semantic":[129],"details.":[130],"In":[131],"addition,":[132],"meta-learning":[133],"techniques":[134],"are":[135],"utilized":[136],"deal":[138],"problems":[140],"data":[145],"enhance":[148],"model\u2019s":[150],"ability":[151],"generalize":[153],"new":[155],"tasks.":[156],"Numerous":[157],"experiments":[158],"show":[159],"that":[160],"Ank-motor":[161],"significantly":[162],"outperforms":[163],"on":[166],"multiple":[167],"benchmark":[168],"datasets.":[169]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
