{"id":"https://openalex.org/W4206925346","doi":"https://doi.org/10.26599/bdma.2021.9020023","title":"News topic detection based on capsule semantic graph","display_name":"News topic detection based on capsule semantic graph","publication_year":2022,"publication_date":"2022-01-24","ids":{"openalex":"https://openalex.org/W4206925346","doi":"https://doi.org/10.26599/bdma.2021.9020023"},"language":"en","primary_location":{"id":"doi:10.26599/bdma.2021.9020023","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2021.9020023","pdf_url":"https://ieeexplore.ieee.org/ielx7/8254253/9691293/09691297.pdf","source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654","2097-406X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ieeexplore.ieee.org/ielx7/8254253/9691293/09691297.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020086523","display_name":"Shuang Yang","orcid":"https://orcid.org/0000-0003-1133-384X"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Yang","raw_affiliation_strings":["College of Computer and Information Science, Southwest University, Chongqing 400000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Information Science, Southwest University, Chongqing 400000, China","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026602746","display_name":"Yan Tang","orcid":"https://orcid.org/0000-0001-5239-5807"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Tang","raw_affiliation_strings":["College of Computer and Information Science, Southwest University, Chongqing 400000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Information Science, Southwest University, Chongqing 400000, China","institution_ids":["https://openalex.org/I142108993"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I142108993"],"apc_list":null,"apc_paid":null,"fwci":3.4071,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.93154472,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"5","issue":"2","first_page":"98","last_page":"109"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9790999889373779,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9754999876022339,"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":0.6381429433822632},{"id":"https://openalex.org/keywords/latent-semantic-analysis","display_name":"Latent semantic analysis","score":0.607226550579071},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.579108476638794},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5554592609405518},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4681328535079956},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.43810707330703735},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4111762046813965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3797028958797455},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3602239489555359},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3528977334499359},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.18616655468940735}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6381429433822632},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.607226550579071},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.579108476638794},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5554592609405518},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4681328535079956},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.43810707330703735},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4111762046813965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3797028958797455},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3602239489555359},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3528977334499359},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.18616655468940735}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.26599/bdma.2021.9020023","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2021.9020023","pdf_url":"https://ieeexplore.ieee.org/ielx7/8254253/9691293/09691297.pdf","source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654","2097-406X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:0f29e6bae1bb40b2bd24506ec9964adf","is_oa":true,"landing_page_url":"https://doaj.org/article/0f29e6bae1bb40b2bd24506ec9964adf","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":"Big Data Mining and Analytics, Vol 5, Iss 2, Pp 98-109 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.26599/bdma.2021.9020023","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2021.9020023","pdf_url":"https://ieeexplore.ieee.org/ielx7/8254253/9691293/09691297.pdf","source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654","2097-406X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4206925346.pdf","grobid_xml":"https://content.openalex.org/works/W4206925346.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1496393218","https://openalex.org/W1525595230","https://openalex.org/W1866975135","https://openalex.org/W1966193717","https://openalex.org/W1989894105","https://openalex.org/W2064988570","https://openalex.org/W2077303393","https://openalex.org/W2107410045","https://openalex.org/W2125050594","https://openalex.org/W2159598522","https://openalex.org/W2161552072","https://openalex.org/W2166202924","https://openalex.org/W2349682613","https://openalex.org/W2407879741","https://openalex.org/W2520355133","https://openalex.org/W2592353946","https://openalex.org/W2603185621","https://openalex.org/W2756654724","https://openalex.org/W2777453820","https://openalex.org/W2807927369","https://openalex.org/W2944467779","https://openalex.org/W3014096468","https://openalex.org/W3025405389","https://openalex.org/W3138845052","https://openalex.org/W4294170691","https://openalex.org/W6600002382","https://openalex.org/W6699929749"],"related_works":["https://openalex.org/W3152143533","https://openalex.org/W3016822073","https://openalex.org/W2921491680","https://openalex.org/W2995939990","https://openalex.org/W2626769217","https://openalex.org/W2996839460","https://openalex.org/W4297006557","https://openalex.org/W2171515436","https://openalex.org/W2981074787","https://openalex.org/W2914864478"],"abstract_inverted_index":{"Most":[0],"news":[1,49,130,194],"topic":[2,22,37,50],"detection":[3,23,38,51],"methods":[4,31,35,209],"use":[5],"word-based":[6],"methods,":[7],"which":[8,78],"easily":[9],"ignore":[10],"the":[11,27,54,69,101,109,115,118,126,129,135,146,153,167,198],"relationship":[12,107,157],"among":[13,108,148],"words":[14],"and":[15,32,160,183,210],"have":[16,39],"semantic":[17,58,106],"sparsity,":[18],"resulting":[19],"in":[20,65],"low":[21],"accuracy.":[24],"In":[25],"addition,":[26],"current":[28],"mainstream":[29],"probability":[30],"graph":[33,59,97,154,212],"analysis":[34,213],"for":[36],"high":[40],"time":[41,71,199],"complexity.":[42],"For":[43],"these":[44],"reasons,":[45],"we":[46],"present":[47],"a":[48,75,90],"model":[52],"on":[53,171,192],"basis":[55],"of":[56,92,103,117,122,201,207],"capsule":[57],"(CSG).":[60],"The":[61,96,105,156],"keywords":[62],"that":[63,144,176,197,206],"appear":[64],"each":[66,123,140],"text":[67,131,141],"at":[68],"same":[70,127],"are":[72],"modeled":[73],"as":[74,100],"keyword":[76],"graph,":[77],"is":[79,98,111,132,150,162,203],"divided":[80],"into":[81],"multiple":[82],"subgraphs":[83],"through":[84],"community":[85],"detection.":[86],"Each":[87],"subgraph":[88],"contains":[89],"group":[91],"closely":[93],"related":[94],"keywords.":[95],"used":[99],"vertex":[102],"CSG.":[104],"vertices":[110,159],"obtained":[112],"by":[113,152],"calculating":[114,166],"similarity":[116,147],"average":[119],"word":[120],"vector":[121],"vertex.":[124],"At":[125],"time,":[128],"clustered":[133],"using":[134],"incremental":[136],"clustering":[137],"method,":[138],"where":[139],"uses":[142],"CSG;":[143],"is,":[145],"texts":[149],"calculated":[151],"kernel.":[155],"between":[158],"edges":[161],"also":[163],"considered":[164],"when":[165],"similarity.":[168],"Experimental":[169,190],"results":[170,191],"three":[172],"standard":[173],"datasets":[174,195],"show":[175],"CSG":[177,202],"can":[178],"obtain":[179],"higher":[180],"precision,":[181],"recall,":[182],"F1":[184],"values":[185],"than":[186,205],"several":[187],"latest":[188],"methods.":[189,214],"large-scale":[193],"reveal":[196],"complexity":[200],"lower":[204],"probabilistic":[208],"other":[211]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
