{"id":"https://openalex.org/W2023096815","doi":"https://doi.org/10.1145/2663761.2664212","title":"A semantic weighting method for document classification based on Markov logic networks","display_name":"A semantic weighting method for document classification based on Markov logic networks","publication_year":2014,"publication_date":"2014-10-05","ids":{"openalex":"https://openalex.org/W2023096815","doi":"https://doi.org/10.1145/2663761.2664212","mag":"2023096815"},"language":"en","primary_location":{"id":"doi:10.1145/2663761.2664212","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2663761.2664212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems","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/A5100375228","display_name":"Eunji Lee","orcid":"https://orcid.org/0000-0001-5916-2301"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Eunji Lee","raw_affiliation_strings":["Chosun University, Gwangju, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Chosun University, Gwangju, Republic of Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101840514","display_name":"Jeongin Kim","orcid":"https://orcid.org/0000-0002-5743-0955"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jeongin Kim","raw_affiliation_strings":["Chosun University, Gwangju, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Chosun University, Gwangju, Republic of Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101407414","display_name":"Junho Choi","orcid":"https://orcid.org/0000-0002-0339-5429"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junho Choi","raw_affiliation_strings":["Chosun University, Gwangju, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Chosun University, Gwangju, Republic of Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101410165","display_name":"Chang Choi","orcid":"https://orcid.org/0000-0002-2276-2378"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chang Choi","raw_affiliation_strings":["Chosun University, Gwangju, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Chosun University, Gwangju, Republic of Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113591246","display_name":"Byeongkyu Ko","orcid":null},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byeongkyu Ko","raw_affiliation_strings":["Chosun University, Gwangju, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Chosun University, Gwangju, Republic of Korea","institution_ids":["https://openalex.org/I152238500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061116975","display_name":"Pankoo Kim","orcid":"https://orcid.org/0000-0003-0111-5152"},"institutions":[{"id":"https://openalex.org/I152238500","display_name":"Chosun University","ror":"https://ror.org/01zt9a375","country_code":"KR","type":"education","lineage":["https://openalex.org/I152238500"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Pankoo Kim","raw_affiliation_strings":["Chosun University, Gwangju, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Chosun University, Gwangju, Republic of Korea","institution_ids":["https://openalex.org/I152238500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100375228"],"corresponding_institution_ids":["https://openalex.org/I152238500"],"apc_list":null,"apc_paid":null,"fwci":0.409,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.73468701,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"29","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9934999942779541,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9934999942779541,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9868000149726868,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.984000027179718,"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":0.8766869306564331},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.8044467568397522},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.656888484954834},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.45824381709098816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44901251792907715}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8766869306564331},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.8044467568397522},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.656888484954834},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.45824381709098816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44901251792907715},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2663761.2664212","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2663761.2664212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1942017069","display_name":null,"funder_award_id":"2013R1A1A2A10011667","funder_id":"https://openalex.org/F4320322349","funder_display_name":"Ministry of Education, Science and Technology"},{"id":"https://openalex.org/G4432089475","display_name":null,"funder_award_id":"2013R1A1A2A10011667","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320322349","display_name":"Ministry of Education, Science and Technology","ror":"https://ror.org/01p262204"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1580305312","https://openalex.org/W1977970897","https://openalex.org/W1988205490","https://openalex.org/W1997945384","https://openalex.org/W2025525983","https://openalex.org/W2060301741","https://openalex.org/W2075728148","https://openalex.org/W2154642793","https://openalex.org/W2188347037","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2049003611","https://openalex.org/W2127804977","https://openalex.org/W2108418243","https://openalex.org/W164103134","https://openalex.org/W2787352659","https://openalex.org/W1970611213"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,20,38,136],"semantic":[4,128,133],"weighting":[5],"method":[6],"to":[7,46,54,117,139],"classify":[8],"textural":[9],"documents.":[10,143,154],"Human":[11],"lives":[12],"in":[13,115,120,164],"the":[14,24,34,55,196,202],"world":[15],"where":[16],"web":[17,43,59],"documents":[18,44,80,94,114],"have":[19,65,89,135],"great":[21,137],"potential":[22,138],"and":[23,95,132,162,182],"amount":[25,57],"of":[26,58,83,152,198],"valuable":[27],"information":[28,134],"has":[29],"been":[30,66,90],"consistently":[31],"growing":[32],"over":[33],"year.":[35],"There":[36],"is":[37,50,76,145],"problem":[39],"that":[40],"finding":[41],"relevant":[42],"corresponding":[45],"what":[47],"users":[48],"want":[49],"more":[51],"difficult":[52],"due":[53],"huge":[56],"size.":[60],"For":[61],"this":[62,70,103,156],"reason,":[63],"there":[64],"many":[67],"researchers":[68],"overcome":[69],"problem.":[71],"The":[72,130,192],"most":[73],"important":[74],"thing":[75],"document":[77,121,161],"classification.":[78,122],"All":[79],"are":[81],"composed":[82],"numerous":[84],"words.":[85],"Many":[86],"classification":[87],"methods":[88],"extracted":[91],"keywords":[92,98,112,125],"from":[93,113,150,159],"then":[96],"analyzed":[97],"pattern":[99],"or":[100],"frequency.":[101],"In":[102],"paper,":[104],"we":[105,147],"propose":[106],"Category":[107,166],"Term":[108,167],"Weight":[109],"(CTW)":[110],"using":[111],"order":[116],"enhance":[118],"performance":[119],"CTW":[123,149,158,163],"combines":[124],"frequency":[126,131],"with":[127,201],"information.":[129],"find":[140],"similarities":[141],"between":[142],"That":[144],"why":[146],"calculates":[148],"collection":[151],"training":[153],"After":[155],"step,":[157],"unknown":[160],"previous":[165],"Database":[168],"will":[169,186],"be":[170,187],"applied":[171,190],"by":[172,189],"designed":[173,179],"Markov":[174],"Logic":[175],"Networks":[176],"Model.":[177],"Our":[178],"MLNs":[180],"Model":[181,185],"existing":[183,203],"Naive-bayse":[184],"compared":[188],"CTW.":[191],"experimental":[193],"results":[194],"shows":[195],"improvement":[197],"precision":[199],"compare":[200],"model.":[204]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
