{"id":"https://openalex.org/W4411161663","doi":"https://doi.org/10.4018/ijdwm.378680","title":"A Heterogeneous Network Text Attribute Fusion Method Based on Multi-Level Semantic Relation Contrastive Learning","display_name":"A Heterogeneous Network Text Attribute Fusion Method Based on Multi-Level Semantic Relation Contrastive Learning","publication_year":2025,"publication_date":"2025-06-10","ids":{"openalex":"https://openalex.org/W4411161663","doi":"https://doi.org/10.4018/ijdwm.378680"},"language":"en","primary_location":{"id":"doi:10.4018/ijdwm.378680","doi":"https://doi.org/10.4018/ijdwm.378680","is_oa":true,"landing_page_url":"https://doi.org/10.4018/ijdwm.378680","pdf_url":"https://www.igi-global.com/ViewTitle.aspx?TitleId=378680&isxn=9798337315669","source":{"id":"https://openalex.org/S53932126","display_name":"International Journal of Data Warehousing and Mining","issn_l":"1548-3924","issn":["1548-3924","1548-3932"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-c","anvur-scientific","cwts-core","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-3"],"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Warehousing and Mining","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W4411161663"],"child_works_count":0,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.igi-global.com/ViewTitle.aspx?TitleId=378680&isxn=9798337315669","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100703373","display_name":"Wei Zhang","orcid":"https://orcid.org/0009-0000-9306-8116","observed_orcids":["https://orcid.org/0009-0000-9306-8116"]},"institutions":[{"id":"https://openalex.org/I20616075","display_name":"Qinghai Normal University","ror":"https://ror.org/03az1t892","country_code":"CN","type":"education","lineage":["https://openalex.org/I20616075"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["Qinghai Normal University, China"],"raw_orcid":"https://orcid.org/0009-0000-9306-8116","affiliations":[{"raw_affiliation_string":"Qinghai Normal University, China","institution_ids":["https://openalex.org/I20616075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036982271","display_name":"Zhonglin Ye","orcid":"https://orcid.org/0000-0002-2429-3325","observed_orcids":["https://orcid.org/0000-0002-2429-3325"]},"institutions":[{"id":"https://openalex.org/I20616075","display_name":"Qinghai Normal University","ror":"https://ror.org/03az1t892","country_code":"CN","type":"education","lineage":["https://openalex.org/I20616075"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhonglin Ye","raw_affiliation_strings":["Qinghai Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-2429-3325","affiliations":[{"raw_affiliation_string":"Qinghai Normal University, China","institution_ids":["https://openalex.org/I20616075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20616075"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":"1","first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994999766349792,"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":0.9994999766349792,"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/T10064","display_name":"Complex Network Analysis Techniques","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/heterogeneous-networks","display_name":"heterogeneous networks","score":0.9670000076293945},{"id":"https://openalex.org/keywords/self-supervised-learning","display_name":"self-supervised learning","score":0.7910000085830688},{"id":"https://openalex.org/keywords/multi-view-alignment","display_name":"multi-view alignment","score":0.5130000114440918}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9223926067352295},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7438583374023438},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6115419268608093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6047504544258118},{"id":"https://openalex.org/C2988080768","wikidata":"https://www.wikidata.org/wiki/Q7095057","display_name":"Semantic relation","level":3,"score":0.5480849146842957},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.47027093172073364},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.4657016396522522},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.4303201138973236},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.20914438366889954},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2014777660369873},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4018/ijdwm.378680","doi":"https://doi.org/10.4018/ijdwm.378680","is_oa":true,"landing_page_url":"https://doi.org/10.4018/ijdwm.378680","pdf_url":"https://www.igi-global.com/ViewTitle.aspx?TitleId=378680&isxn=9798337315669","source":{"id":"https://openalex.org/S53932126","display_name":"International Journal of Data Warehousing and Mining","issn_l":"1548-3924","issn":["1548-3924","1548-3932"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-c","anvur-scientific","cwts-core","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-3"],"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Warehousing and Mining","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.4018/ijdwm.378680","doi":"https://doi.org/10.4018/ijdwm.378680","is_oa":true,"landing_page_url":"https://doi.org/10.4018/ijdwm.378680","pdf_url":"https://www.igi-global.com/ViewTitle.aspx?TitleId=378680&isxn=9798337315669","source":{"id":"https://openalex.org/S53932126","display_name":"International Journal of Data Warehousing and Mining","issn_l":"1548-3924","issn":["1548-3924","1548-3932"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-c","anvur-scientific","cwts-core","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-3"],"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Warehousing and Mining","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1474490597","display_name":null,"funder_award_id":"111 Project","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4451983844","display_name":null,"funder_award_id":"D20035","funder_id":"https://openalex.org/F4320327066","funder_display_name":"Qinghai Normal University"},{"id":"https://openalex.org/G8949795292","display_name":null,"funder_award_id":"92470202","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/F4320327066","display_name":"Qinghai Normal University","ror":"https://ror.org/03az1t892"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411161663.pdf","grobid_xml":"https://content.openalex.org/works/W4411161663.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W2154851992","https://openalex.org/W2173027866","https://openalex.org/W2393319904","https://openalex.org/W2465015709","https://openalex.org/W2519887557","https://openalex.org/W2604314403","https://openalex.org/W2607500032","https://openalex.org/W2624431344","https://openalex.org/W2766453196","https://openalex.org/W2788512147","https://openalex.org/W2792839479","https://openalex.org/W2811124557","https://openalex.org/W2893944917","https://openalex.org/W2911286998","https://openalex.org/W2917620985","https://openalex.org/W2939208918","https://openalex.org/W2962756421","https://openalex.org/W2963600651","https://openalex.org/W3004507689","https://openalex.org/W3033039844","https://openalex.org/W3035719901","https://openalex.org/W3039075121","https://openalex.org/W3082368324","https://openalex.org/W3094497296","https://openalex.org/W3095746859","https://openalex.org/W3176508070","https://openalex.org/W3195415198","https://openalex.org/W4221088475","https://openalex.org/W4225412853","https://openalex.org/W4281779196","https://openalex.org/W4297808394","https://openalex.org/W4309874040","https://openalex.org/W4389092500","https://openalex.org/W4400447734"],"related_works":["https://openalex.org/W4392445505","https://openalex.org/W4411957819","https://openalex.org/W4409687152","https://openalex.org/W4285821847","https://openalex.org/W4411343827","https://openalex.org/W7164071979","https://openalex.org/W4214899340","https://openalex.org/W7161614984","https://openalex.org/W7124458108","https://openalex.org/W4298028950"],"abstract_inverted_index":{"Contrastive":[0],"learning":[1,11,82],"enables":[2],"models":[3],"to":[4,90,106,114],"learn":[5],"graph":[6,23],"structural":[7,24],"information":[8,25,131],"through":[9],"self-supervised":[10],"in":[12,108],"the":[13,52,80,85,92,98,102,116,128],"absence":[14],"of":[15,71,77,84,97,130],"labels.":[16],"However,":[17],"real-world":[18],"networks":[19,105],"often":[20],"contain":[21],"both":[22],"and":[26,60],"incomplete":[27],"node":[28],"attribute":[29,41,62,121],"information.":[30],"Based":[31],"on":[32,45],"this,":[33],"this":[34,109],"paper":[35],"proposes":[36],"a":[37],"heterogeneous":[38,53,86,99,104],"network":[39,54,87],"text":[40,72,120],"fusion":[42,129],"method":[43],"based":[44],"multi-layer":[46],"semantic":[47,59,69],"relation":[48],"contrastive":[49,81],"learning.":[50],"Firstly,":[51],"components":[55],"are":[56],"reconstructed":[57],"using":[58],"thematic":[61],"acquisition":[63],"methods":[64],"at":[65,74],"different":[66,95,124],"levels,":[67],"obtaining":[68],"representations":[70],"attributes":[73],"various":[75],"levels":[76],"abstraction.":[78],"Then,":[79],"component":[83],"is":[88],"employed":[89],"maximize":[91],"correlation":[93],"between":[94,119,132],"views":[96],"network,":[100],"allowing":[101],"two":[103],"align":[107],"space.":[110],"This":[111],"alignment":[112],"helps":[113],"uncover":[115],"latent":[117],"connections":[118],"features":[122],"across":[123],"views,":[125],"thereby":[126],"achieving":[127],"views.":[133]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2025-10-10T00:00:00"}