{"id":"https://openalex.org/W4318147829","doi":"https://doi.org/10.1109/bigdata55660.2022.10020536","title":"Workflow for Domain- and Task-Sensitive Curation of Knowledge Graphs, with Use Case of DRKG","display_name":"Workflow for Domain- and Task-Sensitive Curation of Knowledge Graphs, with Use Case of DRKG","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147829","doi":"https://doi.org/10.1109/bigdata55660.2022.10020536"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020536","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5045531668","display_name":"Kara Schatz","orcid":"https://orcid.org/0000-0003-2310-5131"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kara Schatz","raw_affiliation_strings":["North Carolina State University,Raleigh,North Carolina,USA","North Carolina State University, Raleigh, North Carolina, USA"],"affiliations":[{"raw_affiliation_string":"North Carolina State University,Raleigh,North Carolina,USA","institution_ids":["https://openalex.org/I137902535"]},{"raw_affiliation_string":"North Carolina State University, Raleigh, North Carolina, USA","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001438691","display_name":"Daniel Korn","orcid":"https://orcid.org/0000-0002-1780-9872"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Korn","raw_affiliation_strings":["University of North Carolina,Chapel Hill,North Carolina,USA","University of North Carolina, Chapel Hill, North Carolina, USA"],"affiliations":[{"raw_affiliation_string":"University of North Carolina,Chapel Hill,North Carolina,USA","institution_ids":["https://openalex.org/I114027177"]},{"raw_affiliation_string":"University of North Carolina, Chapel Hill, North Carolina, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078536199","display_name":"Alexander Tropsha","orcid":"https://orcid.org/0000-0003-3802-8896"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Tropsha","raw_affiliation_strings":["University of North Carolina,Chapel Hill,North Carolina,USA","University of North Carolina, Chapel Hill, North Carolina, USA"],"affiliations":[{"raw_affiliation_string":"University of North Carolina,Chapel Hill,North Carolina,USA","institution_ids":["https://openalex.org/I114027177"]},{"raw_affiliation_string":"University of North Carolina, Chapel Hill, North Carolina, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078910758","display_name":"Rada Chirkova","orcid":"https://orcid.org/0000-0003-4249-9690"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rada Chirkova","raw_affiliation_strings":["North Carolina State University,Raleigh,North Carolina,USA","North Carolina State University, Raleigh, North Carolina, USA"],"affiliations":[{"raw_affiliation_string":"North Carolina State University,Raleigh,North Carolina,USA","institution_ids":["https://openalex.org/I137902535"]},{"raw_affiliation_string":"North Carolina State University, Raleigh, North Carolina, USA","institution_ids":["https://openalex.org/I137902535"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5045531668"],"corresponding_institution_ids":["https://openalex.org/I137902535"],"apc_list":null,"apc_paid":null,"fwci":0.4155,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60277957,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3692","last_page":"3701"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.995199978351593,"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":0.995199978351593,"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":0.9944999814033508,"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":0.9914000034332275,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8240140080451965},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.7977102994918823},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.5824933648109436},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.5640283823013306},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5390090346336365},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4841404855251312},{"id":"https://openalex.org/keywords/usable","display_name":"USable","score":0.4583941698074341},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.4375108778476715},{"id":"https://openalex.org/keywords/knowledge-management","display_name":"Knowledge management","score":0.3476805090904236},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.34042781591415405},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.20778053998947144},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.197942852973938},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.17331182956695557}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8240140080451965},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.7977102994918823},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5824933648109436},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.5640283823013306},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5390090346336365},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4841404855251312},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.4583941698074341},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4375108778476715},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.3476805090904236},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.34042781591415405},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.20778053998947144},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.197942852973938},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.17331182956695557}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020536","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1975147762","https://openalex.org/W2087563523","https://openalex.org/W2151589533","https://openalex.org/W2242464395","https://openalex.org/W2300469216","https://openalex.org/W2528837208","https://openalex.org/W2918428907","https://openalex.org/W2949311246","https://openalex.org/W2964221236","https://openalex.org/W2981150963","https://openalex.org/W2990374909","https://openalex.org/W3003401769","https://openalex.org/W3015409108","https://openalex.org/W3196747654","https://openalex.org/W4206937052","https://openalex.org/W4281704579"],"related_works":["https://openalex.org/W2463183163","https://openalex.org/W2019038080","https://openalex.org/W4292070284","https://openalex.org/W4312933959","https://openalex.org/W4386298164","https://openalex.org/W2981581701","https://openalex.org/W4229080059","https://openalex.org/W4286257253","https://openalex.org/W2916853871","https://openalex.org/W2798664319"],"abstract_inverted_index":{"Recently,":[0],"knowledge":[1,32,58,65,81,94,111,138,215],"graphs":[2,33,59],"have":[3],"seen":[4],"a":[5,11,19,72,137],"significant":[6],"increase":[7],"in":[8,10,167],"popularity":[9],"wide":[12],"variety":[13],"of":[14,57,79,109,124,136,175,213],"domains,":[15,200],"as":[16,153],"they":[17],"provide":[18,140],"basis":[20],"for":[21,60,74],"many":[22,31],"data-analytics":[23],"and":[24,46,55,64,76,83,117,127,133,177,181,193,201,207,210],"knowledge-discovery":[25],"approaches.":[26],"At":[27],"the":[28,53,92,122,144,148,157,162,168,172,179,185,190],"same":[29],"time,":[30],"are":[34],"not":[35],"immediately":[36],"usable":[37],"due":[38],"to":[39,52,105,160,198],"their":[40],"format,":[41,147],"unreadable":[42],"or":[43],"missing":[44],"data,":[45],"inaccessibility.":[47],"These":[48],"issues":[49,108],"present":[50,71,156],"barriers":[51],"exploration":[54,209],"use":[56,173,212],"big":[61,219],"data":[62,115,220],"analytics":[63],"discovery.":[66],"In":[67],"this":[68,89],"paper":[69],"we":[70],"workflow":[73,90,103,150,192,204],"domain-":[75],"task-sensitive":[77],"curation":[78,118],"large-scale":[80,214],"graphs,":[82,112,216],"detail":[84,164],"our":[85,165,203],"experience":[86,166],"with":[87,91,121,171],"implementing":[88],"biomedical":[93,169],"graph":[95,139,146],"called":[96],"Drug":[97],"Repurposing":[98],"Knowledge":[99],"Graph":[100],"(DRKG).":[101],"The":[102],"aims":[104],"address":[106],"usability-related":[107],"real-life":[110],"by":[113],"performing":[114],"setup":[116],"that":[119,130,189,202],"align":[120],"needs":[123],"specific":[125],"tasks":[126],"domains.":[128],"Recognizing":[129],"domain":[131,170],"experts":[132],"anticipated":[134],"users":[135],"invaluable":[141],"expertise":[142],"regarding":[143],"desired":[145],"proposed":[149,191],"involves":[151],"them":[152],"humans-in-the-loop.":[154],"We":[155,187],"processes":[158],"required":[159],"execute":[161],"workflow,":[163],"case":[174],"DRKG,":[176],"discuss":[178],"challenges":[180],"lessons":[182],"learned":[183],"throughout":[184],"experience.":[186],"anticipate":[188],"experiences":[194],"will":[195,205],"be":[196],"applicable":[197],"other":[199],"enable":[206],"encourage":[208],"wider":[211],"thereby":[217],"improving":[218],"analytics.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
