{"id":"https://openalex.org/W2019702931","doi":"https://doi.org/10.1109/35021bigcomp.2015.7072818","title":"Exploring concept graphs for biomedical literature mining","display_name":"Exploring concept graphs for biomedical literature mining","publication_year":2015,"publication_date":"2015-02-01","ids":{"openalex":"https://openalex.org/W2019702931","doi":"https://doi.org/10.1109/35021bigcomp.2015.7072818","mag":"2019702931"},"language":"en","primary_location":{"id":"doi:10.1109/35021bigcomp.2015.7072818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/35021bigcomp.2015.7072818","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Big Data and Smart Computing (BIGCOMP)","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/A5045749444","display_name":"Min Song","orcid":"https://orcid.org/0000-0003-3255-1600"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Min Song","raw_affiliation_strings":["Department of Library and Information Science, Yonsei University, Seoul, South Korea","(Department of Library and Information Science, Yonsei University, Seoul, South Korea)"],"affiliations":[{"raw_affiliation_string":"Department of Library and Information Science, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"(Department of Library and Information Science, Yonsei University, Seoul, South Korea)","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5045749444"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":1.2943,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.85577898,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"17","issue":null,"first_page":"103","last_page":"110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9998999834060669,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9998999834060669,"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":0.9965999722480774,"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"}},{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9675999879837036,"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":0.8335623741149902},{"id":"https://openalex.org/keywords/centrality","display_name":"Centrality","score":0.6864084005355835},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.6728287935256958},{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.6059520840644836},{"id":"https://openalex.org/keywords/phrase","display_name":"Phrase","score":0.5583620071411133},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5207602977752686},{"id":"https://openalex.org/keywords/unified-medical-language-system","display_name":"Unified Medical Language System","score":0.5204839706420898},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.46800509095191956},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4532516896724701},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4310379922389984},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3644028604030609},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31558746099472046},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.22829961776733398}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8335623741149902},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.6864084005355835},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6728287935256958},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.6059520840644836},{"id":"https://openalex.org/C2776224158","wikidata":"https://www.wikidata.org/wiki/Q187931","display_name":"Phrase","level":2,"score":0.5583620071411133},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5207602977752686},{"id":"https://openalex.org/C69505689","wikidata":"https://www.wikidata.org/wiki/Q455338","display_name":"Unified Medical Language System","level":2,"score":0.5204839706420898},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.46800509095191956},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4532516896724701},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4310379922389984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3644028604030609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31558746099472046},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.22829961776733398},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/35021bigcomp.2015.7072818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/35021bigcomp.2015.7072818","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Big Data and Smart Computing (BIGCOMP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W148396368","https://openalex.org/W204556058","https://openalex.org/W296703532","https://openalex.org/W1550258693","https://openalex.org/W1966860954","https://openalex.org/W1969737635","https://openalex.org/W1978558026","https://openalex.org/W2000918452","https://openalex.org/W2015469910","https://openalex.org/W2016085990","https://openalex.org/W2064418625","https://openalex.org/W2067703482","https://openalex.org/W2099908986","https://openalex.org/W2108094535","https://openalex.org/W2113376247","https://openalex.org/W2123005130","https://openalex.org/W2123107811","https://openalex.org/W2127683092","https://openalex.org/W2138621811","https://openalex.org/W2138954094","https://openalex.org/W2139153206","https://openalex.org/W2143114753","https://openalex.org/W2145766604","https://openalex.org/W2163107094","https://openalex.org/W2189675008","https://openalex.org/W2621985024","https://openalex.org/W4233769451","https://openalex.org/W6608313838","https://openalex.org/W6610735226","https://openalex.org/W6632766574","https://openalex.org/W6675091282","https://openalex.org/W6676989618","https://openalex.org/W6680526941","https://openalex.org/W6687375622"],"related_works":["https://openalex.org/W4229078645","https://openalex.org/W1977345676","https://openalex.org/W79619734","https://openalex.org/W4282032776","https://openalex.org/W2010487328","https://openalex.org/W2340589664","https://openalex.org/W4281750475","https://openalex.org/W3087321790","https://openalex.org/W2395081922","https://openalex.org/W2409065517"],"abstract_inverted_index":{"Full-text":[0],"publications":[1],"in":[2,33,60,191,206],"an":[3],"electronic":[4,62],"form":[5],"become":[6],"more":[7],"prevalent":[8],"than":[9],"ever":[10],"before.":[11],"It":[12],"is":[13,96,119],"a":[14,57,73,80,131,166,188],"difficult":[15],"challenge":[16,41],"to":[17,97,139,175,181],"extract":[18],"concepts":[19,27,44,121,178],"from":[20],"unstructured":[21],"document":[22,53],"collections":[23,54],"data":[24],"because":[25],"different":[26,141],"and":[28,35,50,55,64,104,112,136,144,184,209],"their":[29],"relationships":[30],"are":[31,45,106,179],"buried":[32],"them":[34],"ample":[36],"term":[37],"variations":[38],"make":[39],"the":[40,93,156,170,173,192,199,211],"compound.":[42],"Extracted":[43],"useful":[46],"instruments":[47],"of":[48,83,133,172,213],"managing":[49],"searching":[51],"large":[52],"play":[56],"pivotal":[58],"role":[59,190],"indexing":[61],"documents":[63,99],"building":[65],"digital":[66],"libraries.":[67],"In":[68],"this":[69,195],"paper":[70],"we":[71,197],"explore":[72],"biomedical":[74,134],"concept":[75,84,186],"extraction":[76,143],"technique":[77,88,129,154],"based":[78],"on":[79],"ranking":[81,146],"algorithm":[82],"graphs.":[85],"The":[86,116,148],"proposed":[87],"comprises":[89],"two":[90],"major":[91],"steps:":[92],"first":[94],"step":[95,118],"represent":[98],"with":[100,122,130,201],"graphs":[101],"whose":[102],"nodes":[103,214],"edges":[105],"created":[107],"by":[108,215],"Named":[109],"Entity":[110],"Recognition":[111],"UMLS":[113],"Semantic":[114],"Network.":[115],"second":[117],"rank":[120],"relative":[123],"importance":[124],"algorithms.":[125,162],"We":[126,163],"evaluate":[127],"our":[128,153],"set":[132],"full-texts":[135],"compare":[137],"it":[138],"various":[140],"key-phrase":[142],"graph":[145],"techniques.":[147],"experimental":[149],"results":[150],"show":[151],"that":[152],"achieves":[155],"best":[157],"performance":[158],"over":[159],"other":[160,183],"compared":[161],"further":[164],"take":[165],"close":[167],"look":[168],"at":[169],"properties":[171],"network":[174,200],"examine":[176],"how":[177],"related":[180],"each":[182],"what":[185],"plays":[187],"dominant":[189],"network.":[193],"To":[194],"end,":[196],"build":[198],"526":[202],"full-text":[203],"articles":[204],"published":[205],"PubMed":[207],"Central":[208],"measure":[210],"significance":[212],"centrality.":[216]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
