{"id":"https://openalex.org/W4388894602","doi":"https://doi.org/10.1109/tencon58879.2023.10322344","title":"Knowledge Graph for Deriving Insights on The Thai Government Dataset","display_name":"Knowledge Graph for Deriving Insights on The Thai Government Dataset","publication_year":2023,"publication_date":"2023-10-31","ids":{"openalex":"https://openalex.org/W4388894602","doi":"https://doi.org/10.1109/tencon58879.2023.10322344"},"language":"en","primary_location":{"id":"doi:10.1109/tencon58879.2023.10322344","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tencon58879.2023.10322344","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"},"type":"article","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/A5093314811","display_name":"K. Saratoon","orcid":null},"institutions":[{"id":"https://openalex.org/I38538140","display_name":"Asian Institute of Technology","ror":"https://ror.org/0403qcr87","country_code":"TH","type":"education","lineage":["https://openalex.org/I38538140"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"K. Saratoon","raw_affiliation_strings":["Asian Institute of Technology,ICT department,Bangkok,Thailand","ICT department, Asian Institute of Technology, Bangkok, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Asian Institute of Technology,ICT department,Bangkok,Thailand","institution_ids":["https://openalex.org/I38538140"]},{"raw_affiliation_string":"ICT department, Asian Institute of Technology, Bangkok, Thailand","institution_ids":["https://openalex.org/I38538140"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093314812","display_name":"A. Chutiporn","orcid":null},"institutions":[{"id":"https://openalex.org/I38538140","display_name":"Asian Institute of Technology","ror":"https://ror.org/0403qcr87","country_code":"TH","type":"education","lineage":["https://openalex.org/I38538140"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"A. Chutiporn","raw_affiliation_strings":["Asian Institute of Technology,ICT department,Bangkok,Thailand","ICT department, Asian Institute of Technology, Bangkok, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Asian Institute of Technology,ICT department,Bangkok,Thailand","institution_ids":["https://openalex.org/I38538140"]},{"raw_affiliation_string":"ICT department, Asian Institute of Technology, Bangkok, Thailand","institution_ids":["https://openalex.org/I38538140"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5093314813","display_name":"S. Nuttapong","orcid":null},"institutions":[{"id":"https://openalex.org/I14316845","display_name":"National Electronics and Computer Technology Center","ror":"https://ror.org/04z82ry91","country_code":"TH","type":"government","lineage":["https://openalex.org/I1332092204","https://openalex.org/I14316845"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"S. Nuttapong","raw_affiliation_strings":["National Electronics and Computer Technology Center (NECTEC),Strategic Analytics Networks with Machine Learning and AI(SAl),Bangkok,Thailand","Strategic Analytics Networks with Machine Learning and AI(SAl), National Electronics and Computer Technology Center (NECTEC), Bangkok, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Electronics and Computer Technology Center (NECTEC),Strategic Analytics Networks with Machine Learning and AI(SAl),Bangkok,Thailand","institution_ids":["https://openalex.org/I14316845"]},{"raw_affiliation_string":"Strategic Analytics Networks with Machine Learning and AI(SAl), National Electronics and Computer Technology Center (NECTEC), Bangkok, Thailand","institution_ids":["https://openalex.org/I14316845"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15243272,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"365","last_page":"370"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9976000189781189,"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":0.9976000189781189,"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":0.9969000220298767,"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/T10028","display_name":"Topic Modeling","score":0.9955000281333923,"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":0.8634960651397705},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6281699538230896},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6204500198364258},{"id":"https://openalex.org/keywords/graph-traversal","display_name":"Graph traversal","score":0.573606550693512},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.5713392496109009},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5211251378059387},{"id":"https://openalex.org/keywords/tree-traversal","display_name":"Tree traversal","score":0.4829641878604889},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.47361990809440613},{"id":"https://openalex.org/keywords/entity-linking","display_name":"Entity linking","score":0.46610650420188904},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4633195102214813},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4612170457839966},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43610838055610657},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.36018306016921997},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.27713167667388916},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1643279492855072},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.16260716319084167}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8634960651397705},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6281699538230896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6204500198364258},{"id":"https://openalex.org/C96333769","wikidata":"https://www.wikidata.org/wiki/Q907955","display_name":"Graph traversal","level":3,"score":0.573606550693512},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5713392496109009},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5211251378059387},{"id":"https://openalex.org/C140745168","wikidata":"https://www.wikidata.org/wiki/Q1210082","display_name":"Tree traversal","level":2,"score":0.4829641878604889},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.47361990809440613},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.46610650420188904},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4633195102214813},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4612170457839966},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43610838055610657},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.36018306016921997},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.27713167667388916},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1643279492855072},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.16260716319084167},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon58879.2023.10322344","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tencon58879.2023.10322344","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2852434548","https://openalex.org/W3015409108","https://openalex.org/W3049596049","https://openalex.org/W3157818492","https://openalex.org/W4210689855","https://openalex.org/W6784104812","https://openalex.org/W7024746193"],"related_works":["https://openalex.org/W2200188075","https://openalex.org/W4252596799","https://openalex.org/W4254594467","https://openalex.org/W4230405657","https://openalex.org/W244044452","https://openalex.org/W2552915643","https://openalex.org/W170547082","https://openalex.org/W3213135344","https://openalex.org/W3183956626","https://openalex.org/W2136735429"],"abstract_inverted_index":{"Natural":[0,166],"language":[1,58,81,167],"processing":[2],"(NLP)":[3],"is":[4,72,82,148,177,204,218,250],"mandatory":[5],"in":[6,88,150,205,229],"working":[7],"with":[8,104],"text.":[9],"There":[10],"are":[11,17,26,100,138,222],"many":[12],"tools":[13,25,46,99,258],"and":[14,102,116,127,173,182,227,236,259],"applications":[15],"that":[16,75,137,248],"based":[18],"on":[19,163],"it.":[20],"However,":[21],"most":[22],"of":[23,66,76,85,93,107,170,201,208,215],"those":[24],"often":[27],"operated":[28],"in,":[29],"or":[30,51,140,160],"only,":[31],"the":[32,67,70,77,91,94,105,111,118,132,146,151,164,187,192,199,206,213,233,239,257,260],"English":[33,78],"language.":[34],"In":[35],"recent":[36],"years,":[37],"there":[38,249],"has":[39],"been":[40],"a":[41,53,157],"continuing":[42],"development":[43],"for":[44,56,59,64,241,253,255],"NLP":[45,61,98],"to":[47,123,143,179,231,238],"support":[48],"other":[49],"languages":[50],"creating":[52,117],"specific":[54],"tool":[55],"certain":[57],"simple":[60],"tasks,":[62,69],"but":[63],"some":[65],"advanced":[68],"advancement":[71],"still":[73,251],"behind":[74],"language,":[79],"Thai":[80,97,112],"also":[83],"one":[84],"them.":[86],"So,":[87],"this":[89],"research,":[90],"capabilities":[92],"currently":[95],"existing":[96],"explored":[101],"evaluated,":[103],"tasks":[106],"extracting":[108],"text":[109],"from":[110,121,131],"government":[113],"dataset":[114],"(eMENSCR)":[115],"knowledge":[119],"graph":[120,152],"it":[122],"improve":[124],"data":[125,147,189],"interpretability":[126],"gain":[128],"more":[129],"insight":[130],"data,":[133],"by":[134,224],"utilizing":[135],"queries":[136],"exclusive,":[139],"less":[141],"complex":[142],"execute,":[144],"when":[145],"stored":[149],"database":[153],"such":[154],"as":[155],"performing":[156],"path":[158],"traversal":[159],"relationship":[161],"counting":[162],"data.":[165],"processing's":[168],"part":[169],"speech":[171],"tagging":[172,176],"named":[174],"entity":[175,181],"used":[178],"perform":[180],"relation":[183],"extraction":[184],"after":[185],"filtering":[186],"unneeded":[188],"fields.":[190],"Then":[191],"extracted":[193],"information":[194],"will":[195],"be":[196],"formulated":[197],"into":[198],"format":[200],"\u201ctriple\u201d,":[202],"which":[203],"form":[207],"(head,":[209],"relation,":[210],"tail).":[211],"After":[212],"process":[214],"triple":[216],"construction":[217],"finished,":[219],"The":[220,244],"triples":[221],"evaluated":[223],"Precision,":[225],"recall,":[226],"F1":[228],"order":[230],"measure":[232],"pipeline's":[234],"performance":[235],"import":[237],"Neo4j":[240],"query":[242],"testing.":[243],"obtained":[245],"results":[246],"show":[247],"room":[252],"improvement":[254],"both":[256],"methodology":[261],"itself.":[262]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
