{"id":"https://openalex.org/W4389352572","doi":"https://doi.org/10.1109/access.2023.3339552","title":"Semi-Supervised Bootstrapped Syntax-Semantics-Based Approach for Agriculture Relation Extraction for Knowledge Graph Creation and Reasoning","display_name":"Semi-Supervised Bootstrapped Syntax-Semantics-Based Approach for Agriculture Relation Extraction for Knowledge Graph Creation and Reasoning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389352572","doi":"https://doi.org/10.1109/access.2023.3339552"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3339552","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3339552","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10343160.pdf","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":null,"license_id":null,"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://ieeexplore.ieee.org/ielx7/6287639/6514899/10343160.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034065331","display_name":"G Veena","orcid":"https://orcid.org/0000-0003-3513-9304"},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"G. Veena","raw_affiliation_strings":["Department of Computer Science and Applications, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, India"],"raw_orcid":"https://orcid.org/0000-0003-3513-9304","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Applications, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, India","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101475086","display_name":"Deepa Gupta","orcid":"https://orcid.org/0000-0002-1041-5125"},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepa Gupta","raw_affiliation_strings":["Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India"],"raw_orcid":"https://orcid.org/0000-0002-1041-5125","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101663690","display_name":"Vani Kanjirangat","orcid":"https://orcid.org/0000-0002-2526-1413"},"institutions":[{"id":"https://openalex.org/I15196421","display_name":"University of Applied Sciences and Arts of Southern Switzerland","ror":"https://ror.org/05ep8g269","country_code":"CH","type":"education","lineage":["https://openalex.org/I15196421"]},{"id":"https://openalex.org/I2614128279","display_name":"Dalle Molle Institute for Artificial Intelligence Research","ror":"https://ror.org/013355g38","country_code":"CH","type":"facility","lineage":["https://openalex.org/I15196421","https://openalex.org/I2614128279","https://openalex.org/I57201433"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Vani Kanjirangat","raw_affiliation_strings":["Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA USI/SUPSI), Viganello, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA USI/SUPSI), Viganello, Switzerland","institution_ids":["https://openalex.org/I2614128279","https://openalex.org/I15196421"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.4474,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.91317138,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"138375","last_page":"138398"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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":0.994700014591217,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9937999844551086,"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/computer-science","display_name":"Computer science","score":0.8159568309783936},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6859305500984192},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5997072458267212},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.5726040005683899},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.5342397093772888},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5228817462921143},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.49597081542015076},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47188058495521545},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.46599140763282776},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.43707531690597534},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.4243980050086975},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4219667315483093},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.4200921058654785},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3780739903450012},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2877765893936157},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.28306400775909424}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8159568309783936},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6859305500984192},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5997072458267212},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.5726040005683899},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.5342397093772888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5228817462921143},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.49597081542015076},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47188058495521545},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.46599140763282776},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.43707531690597534},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.4243980050086975},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4219667315483093},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.4200921058654785},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3780739903450012},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2877765893936157},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.28306400775909424},{"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":2,"locations":[{"id":"doi:10.1109/access.2023.3339552","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3339552","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10343160.pdf","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:985cc49512034479ae8bc97c33757601","is_oa":true,"landing_page_url":"https://doaj.org/article/985cc49512034479ae8bc97c33757601","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 138375-138398 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3339552","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3339552","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10343160.pdf","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":null,"license_id":null,"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/2","display_name":"Zero hunger","score":0.7799999713897705}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389352572.pdf","grobid_xml":"https://content.openalex.org/works/W4389352572.grobid-xml"},"referenced_works_count":116,"referenced_works":["https://openalex.org/W1489949474","https://openalex.org/W1518020032","https://openalex.org/W1529731474","https://openalex.org/W1552847225","https://openalex.org/W1880262756","https://openalex.org/W1918405426","https://openalex.org/W2000441790","https://openalex.org/W2008076627","https://openalex.org/W2015411690","https://openalex.org/W2016753842","https://openalex.org/W2020278455","https://openalex.org/W2040647304","https://openalex.org/W2068882115","https://openalex.org/W2081789618","https://openalex.org/W2083412062","https://openalex.org/W2090565883","https://openalex.org/W2094728533","https://openalex.org/W2097960255","https://openalex.org/W2103931177","https://openalex.org/W2117813082","https://openalex.org/W2119332773","https://openalex.org/W2123442489","https://openalex.org/W2124657827","https://openalex.org/W2145386458","https://openalex.org/W2146327280","https://openalex.org/W2149713870","https://openalex.org/W2163922914","https://openalex.org/W2184957013","https://openalex.org/W2250560707","https://openalex.org/W2251913848","https://openalex.org/W2283196293","https://openalex.org/W2469792262","https://openalex.org/W2518577968","https://openalex.org/W2521367263","https://openalex.org/W2598931156","https://openalex.org/W2600659824","https://openalex.org/W2728059831","https://openalex.org/W2735354302","https://openalex.org/W2738031524","https://openalex.org/W2757101400","https://openalex.org/W2777063490","https://openalex.org/W2794082914","https://openalex.org/W2795113572","https://openalex.org/W2795129839","https://openalex.org/W2810576820","https://openalex.org/W2852434548","https://openalex.org/W2897509371","https://openalex.org/W2898256678","https://openalex.org/W2901387348","https://openalex.org/W2907510403","https://openalex.org/W2907626954","https://openalex.org/W2914592219","https://openalex.org/W2942364792","https://openalex.org/W2949972983","https://openalex.org/W2950339735","https://openalex.org/W2963203544","https://openalex.org/W2963691697","https://openalex.org/W2963738950","https://openalex.org/W2987063152","https://openalex.org/W2997897037","https://openalex.org/W3003265726","https://openalex.org/W3003503920","https://openalex.org/W3014453139","https://openalex.org/W3033028194","https://openalex.org/W3033353720","https://openalex.org/W3033896008","https://openalex.org/W3035134435","https://openalex.org/W3091993229","https://openalex.org/W3096932862","https://openalex.org/W3103296573","https://openalex.org/W3106690387","https://openalex.org/W3113332183","https://openalex.org/W3114456282","https://openalex.org/W3128680570","https://openalex.org/W3138149011","https://openalex.org/W3156082048","https://openalex.org/W3159316741","https://openalex.org/W3175989614","https://openalex.org/W3177015224","https://openalex.org/W3198824379","https://openalex.org/W4206039709","https://openalex.org/W4206093715","https://openalex.org/W4206603474","https://openalex.org/W4210956481","https://openalex.org/W4211043794","https://openalex.org/W4226157755","https://openalex.org/W4230454915","https://openalex.org/W4251372957","https://openalex.org/W4285020859","https://openalex.org/W4294216483","https://openalex.org/W4311772362","https://openalex.org/W4312044727","https://openalex.org/W4316466119","https://openalex.org/W4322500896","https://openalex.org/W4323520833","https://openalex.org/W4376130103","https://openalex.org/W4377695655","https://openalex.org/W4379380529","https://openalex.org/W6604173257","https://openalex.org/W6629638141","https://openalex.org/W6631964550","https://openalex.org/W6639619044","https://openalex.org/W6678778854","https://openalex.org/W6678830454","https://openalex.org/W6679243379","https://openalex.org/W6683761811","https://openalex.org/W6684350123","https://openalex.org/W6718112784","https://openalex.org/W6734915995","https://openalex.org/W6739901393","https://openalex.org/W6749422740","https://openalex.org/W6752392434","https://openalex.org/W6758075616","https://openalex.org/W6761783306","https://openalex.org/W6780896692","https://openalex.org/W6786058255"],"related_works":["https://openalex.org/W2352298027","https://openalex.org/W4319940250","https://openalex.org/W842810586","https://openalex.org/W2092919065","https://openalex.org/W4236762297","https://openalex.org/W3138801416","https://openalex.org/W2369351710","https://openalex.org/W2594363579","https://openalex.org/W2169232658","https://openalex.org/W2444550338"],"abstract_inverted_index":{"We":[0,69,213],"propose":[1],"a":[2,17,22,167,207],"novel":[3],"approach":[4,57,242],"that":[5,221,228,240],"uses":[6],"semi-supervised":[7],"learning":[8],"to":[9,37,74,182],"extract":[10,75],"triplets":[11,247],"from":[12,152],"domain-specific":[13],"texts":[14],"and":[15,50,79,120,126,248],"create":[16,144],"Knowledge":[18],"Graph":[19],"(KG),":[20],"with":[21,87,123],"focus":[23],"on":[24,60,111,175],"the":[25,51,64,106,134,139,158,176,194,223,234,253],"agricultural":[26,107,254],"domain.":[27,255],"Building":[28],"domain":[29,42,54,108,136],"specific":[30,43],"knowledge":[31,67,100,204,217,227,235,250],"graphs":[32,251],"can":[33],"be":[34],"challenging":[35],"due":[36],"several":[38],"factors":[39],"such":[40],"as":[41],"vocabulary,":[44],"data":[45],"integration":[46],"challenges,":[47],"dynamic":[48],"data,":[49],"need":[52],"for":[53,63,92,190,252],"expertise.":[55],"Our":[56],"primarily":[58],"focuses":[59],"triplet":[61,162,208],"extraction":[62,163],"creation":[65],"of":[66,84,105,160,188,196,210,225],"graph.":[68,236],"employ":[70],"dependency":[71],"parsing":[72],"techniques":[73],"relationships":[76],"between":[77],"entities,":[78],"utilize":[80],"an":[81,184,202],"extended":[82],"version":[83],"BERT,":[85],"combined":[86],"Latent":[88],"Dirichlet":[89],"Allocation":[90],"(LDA)":[91],"Named":[93],"Entity":[94],"Recognition":[95],"(NER).":[96],"The":[97],"proposed":[98],"Agriculture":[99,203],"graph":[101,205,218],"covers":[102],"significant":[103],"areas":[104],"by":[109],"focusing":[110],"six":[112],"major":[113,140],"entities:":[114],"soil,":[115],"place,":[116],"disease,":[117],"pathogen,":[118],"pesticide,":[119],"crops,":[121],"along":[122],"their":[124],"intra":[125],"inter-relationships.":[127],"There":[128],"is":[129,229,243],"no":[130],"benchmark":[131],"dataset":[132],"in":[133,233,245],"agriculture":[135,154,172],"encompassing":[137],"all":[138],"entities.":[141],"Hence":[142],"we":[143,165,179,200],"our":[145,161,197,241],"own":[146],"corpus":[147,169,209],"comprises":[148],"30k":[149],"sentences":[150],"sourced":[151],"reputable":[153],"websites.":[155],"To":[156],"evaluate":[157],"effectiveness":[159],"model,":[164],"utilized":[166],"test":[168],"containing":[170],"3500":[171],"triplets.":[173,212],"Based":[174],"experimental":[177],"results,":[178],"were":[180],"able":[181],"achieve":[183],"average":[185],"macro":[186],"F-score":[187],"87%":[189],"relation":[191],"extraction,":[192],"indicating":[193],"efficacy":[195],"approach.":[198],"Additionally,":[199],"created":[201],"using":[206],"6236":[211],"also":[214],"analyzed":[215],"various":[216],"reasoning":[219,249],"models":[220],"improve":[222],"discovery":[224],"implicit":[226],"not":[230],"explicitly":[231],"represented":[232],"Experimental":[237],"results":[238],"indicate":[239],"effective":[244],"creating":[246]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}