{"id":"https://openalex.org/W7127129453","doi":"https://doi.org/10.1109/access.2026.3660183","title":"Ontology Synthesis Using Semi-Automatic Semantic AI Using Deep Learning and Reinforcement Learning","display_name":"Ontology Synthesis Using Semi-Automatic Semantic AI Using Deep Learning and Reinforcement Learning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7127129453","doi":"https://doi.org/10.1109/access.2026.3660183"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3660183","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660183","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3660183","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124849987","display_name":"Gerard Deepak","orcid":null},"institutions":[{"id":"https://openalex.org/I8977528","display_name":"Dr. Hari Singh Gour University","ror":"https://ror.org/01xapxe37","country_code":"IN","type":"education","lineage":["https://openalex.org/I8977528"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Gerard Deepak","raw_affiliation_strings":["1Department of Computer Science and Engineering-Cyber Security, Dayananda Sagar Academy of Technology and Management (DSATM), Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"1Department of Computer Science and Engineering-Cyber Security, Dayananda Sagar Academy of Technology and Management (DSATM), Bengaluru, India","institution_ids":["https://openalex.org/I8977528"]}]},{"author_position":"middle","author":{"id":null,"display_name":"M. S. Roopa","orcid":"https://orcid.org/0000-0002-9752-2037"},"institutions":[{"id":"https://openalex.org/I1333540553","display_name":"Nitte University","ror":"https://ror.org/029nydt37","country_code":"IN","type":"education","lineage":["https://openalex.org/I1333540553"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. S. Roopa","raw_affiliation_strings":["2Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to be University), Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"2Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to be University), Bengaluru, India","institution_ids":["https://openalex.org/I1333540553"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Anubrat Bora","orcid":null},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anubrat Bora","raw_affiliation_strings":["3School of Computer Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India"],"affiliations":[{"raw_affiliation_string":"3School of Computer Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124751650","display_name":"K R Venugopal","orcid":null},"institutions":[{"id":"https://openalex.org/I4210129296","display_name":"Av Engineering (Czechia)","ror":"https://ror.org/02e18ra65","country_code":"CZ","type":"company","lineage":["https://openalex.org/I4210129296"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"K. R. Venugopal","raw_affiliation_strings":["4Department of Computer Science and Engineering, UVCE, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"4Department of Computer Science and Engineering, UVCE, Bengaluru, India","institution_ids":["https://openalex.org/I4210129296"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5124849987"],"corresponding_institution_ids":["https://openalex.org/I8977528"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31513205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"19734","last_page":"19749"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.5443000197410583,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.5443000197410583,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.10029999911785126,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.02459999918937683,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ontology","display_name":"Ontology","score":0.6097999811172485},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.4668000042438507},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.41760000586509705},{"id":"https://openalex.org/keywords/ontology-learning","display_name":"Ontology learning","score":0.4131999909877777},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.39980000257492065},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3937999904155731},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.38519999384880066},{"id":"https://openalex.org/keywords/upper-ontology","display_name":"Upper ontology","score":0.3799999952316284},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.37709999084472656},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3650999963283539}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8547000288963318},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6276000142097473},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.6097999811172485},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.4668000042438507},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.42239999771118164},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C2777002027","wikidata":"https://www.wikidata.org/wiki/Q3620938","display_name":"Ontology learning","level":5,"score":0.4131999909877777},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4106999933719635},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4077000021934509},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C78726541","wikidata":"https://www.wikidata.org/wiki/Q3882785","display_name":"Upper ontology","level":3,"score":0.3799999952316284},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.37709999084472656},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.36090001463890076},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C50382505","wikidata":"https://www.wikidata.org/wiki/Q2553356","display_name":"OWL-S","level":4,"score":0.3447999954223633},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C22550185","wikidata":"https://www.wikidata.org/wiki/Q7095047","display_name":"Ontology-based data integration","level":3,"score":0.30660000443458557},{"id":"https://openalex.org/C101230327","wikidata":"https://www.wikidata.org/wiki/Q826165","display_name":"Web Ontology Language","level":3,"score":0.2948000133037567},{"id":"https://openalex.org/C137982476","wikidata":"https://www.wikidata.org/wiki/Q7072326","display_name":"Open Biomedical Ontologies","level":5,"score":0.2919999957084656},{"id":"https://openalex.org/C166423231","wikidata":"https://www.wikidata.org/wiki/Q1891170","display_name":"Semantic search","level":3,"score":0.2896000146865845},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C115408247","wikidata":"https://www.wikidata.org/wiki/Q3882789","display_name":"Ontology Inference Layer","level":5,"score":0.26649999618530273},{"id":"https://openalex.org/C81758059","wikidata":"https://www.wikidata.org/wiki/Q796584","display_name":"tf\u2013idf","level":3,"score":0.2574000060558319},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3660183","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660183","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e16e76d599994d94b152ac42d3e81b68","is_oa":true,"landing_page_url":"https://doaj.org/article/e16e76d599994d94b152ac42d3e81b68","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 19734-19749 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3660183","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660183","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.593200147151947}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1975885865","https://openalex.org/W2000982518","https://openalex.org/W2008179902","https://openalex.org/W2024384612","https://openalex.org/W2051945466","https://openalex.org/W2109296039","https://openalex.org/W2116591658","https://openalex.org/W2132537081","https://openalex.org/W2166314301","https://openalex.org/W2760712285","https://openalex.org/W2889150252","https://openalex.org/W2913707177","https://openalex.org/W2963655790","https://openalex.org/W2965997714","https://openalex.org/W2980955840","https://openalex.org/W2988839538","https://openalex.org/W3080628442","https://openalex.org/W3092613928","https://openalex.org/W3124627165","https://openalex.org/W3199532808","https://openalex.org/W3215599159","https://openalex.org/W4250056251","https://openalex.org/W4379616184","https://openalex.org/W4380520070"],"related_works":[],"abstract_inverted_index":{"The":[0,94,146],"need":[1],"for":[2,75,124,197,203],"automatic":[3,198],"ontology":[4,199],"modeling":[5],"has":[6],"become":[7],"particularly":[8],"significant":[9],"in":[10,83],"the":[11,20,44,70,104,135,157,174,182,204],"era":[12],"of":[13,96,148,171,206],"Web":[14,51],"3.0,":[15,52],"where":[16],"ontologies":[17],"serve":[18],"as":[19,30,141],"structural":[21],"metadata":[22],"backbone.":[23],"Despite":[24],"their":[25,62],"importance,":[26],"certain":[27],"domains":[28],"such":[29],"environmental":[31,207],"studies,":[32],"though":[33],"common\u2014have":[34],"been":[35],"neglected":[36],"and":[37,61,168],"lack":[38],"formal":[39],"ontologies.":[40],"To":[41,115],"address":[42],"this,":[43,69],"proposed":[45,183],"model":[46,105,158,196],"leverages":[47],"auxiliary":[48,161],"knowledge":[49,113],"from":[50,59],"beginning":[53],"with":[54,151,159,173],"seed":[55],"term":[56],"pool":[57],"generation":[58],"datasets":[60],"classification":[63],"using":[64,188],"a":[65,142,194],"Bi-LSTM":[66],"model.":[67],"Following":[68],"AdaBoost":[71],"classifier":[72],"is":[73,122,139],"employed":[74],"dataset":[76],"classification,":[77],"while":[78],"Pearson\u2019s":[79],"correlation":[80],"coefficient":[81],"aids":[82],"feature":[84],"extraction":[85],"through":[86,130],"federation":[87],"between":[88],"two":[89],"classifiers,":[90],"enabling":[91],"sedated":[92],"learning.":[93],"integration":[95],"Google\u2019s":[97,152],"Knowledge":[98,153],"Graph":[99,154],"Search":[100],"API":[101,155],"further":[102],"enhances":[103],"by":[106],"incorporating":[107],"additional":[108],"information,":[109],"thereby":[110],"strengthening":[111],"its":[112],"infrastructure.":[114],"ensure":[116],"semantic":[117,131],"accuracy,":[118],"Morista\u2019s":[119],"Overlap":[120],"Index":[121],"applied":[123],"semantics-oriented":[125],"relevance":[126],"computations,":[127],"supporting":[128],"reasoning":[129],"similarity":[132],"measures.":[133],"Additionally,":[134],"Imperialist":[136],"Competitive":[137],"Algorithm":[138],"adopted":[140],"metaheuristic":[143],"optimization":[144],"strategy.":[145],"use":[147],"YAGO":[149],"along":[150],"enriches":[156],"dense":[160],"knowledge,":[162],"ultimately":[163],"leading":[164],"to":[165],"high":[166],"precision,":[167],"reliable":[169],"estimation":[170],"F-measure":[172],"lowest":[175],"False":[176],"Discovery":[177],"Rate":[178],"(FDR).":[179],"In":[180],"summary,":[181],"framework":[184],"named":[185],"Ontology":[186],"Synthesis":[187],"Semi-Automatic":[189],"Semantic":[190],"AI":[191],"(OSSS)":[192],"represents":[193],"state-of-the-art":[195],"generation,":[200],"specifically":[201],"tailored":[202],"domain":[205],"journalism.":[208]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-02-03T00:00:00"}