{"id":"https://openalex.org/W3047151099","doi":"https://doi.org/10.1109/percomworkshops48775.2020.9156117","title":"A Further Step Towards Automatic Domain Modelling by Relevant Information Extraction","display_name":"A Further Step Towards Automatic Domain Modelling by Relevant Information Extraction","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3047151099","doi":"https://doi.org/10.1109/percomworkshops48775.2020.9156117","mag":"3047151099"},"language":"en","primary_location":{"id":"doi:10.1109/percomworkshops48775.2020.9156117","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percomworkshops48775.2020.9156117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","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/A5075317929","display_name":"Lars Krupp","orcid":"https://orcid.org/0000-0001-6294-2915"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lars Krupp","raw_affiliation_strings":["Embedded Intelligence, DKFI Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"Embedded Intelligence, DKFI Kaiserslautern, Kaiserslautern, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087174264","display_name":"Gernot Bahle","orcid":"https://orcid.org/0000-0003-4583-0625"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gernot Bahle","raw_affiliation_strings":["Embedded Intelligence, DKFI Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"Embedded Intelligence, DKFI Kaiserslautern, Kaiserslautern, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034531247","display_name":"Agnes Gruenerbl","orcid":"https://orcid.org/0000-0002-4156-7121"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agnes Gruenerbl","raw_affiliation_strings":["Embedded Intelligence, DKFI Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"Embedded Intelligence, DKFI Kaiserslautern, Kaiserslautern, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063453660","display_name":"Paul Lukowicz","orcid":"https://orcid.org/0000-0003-0320-6656"},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Paul Lukowicz","raw_affiliation_strings":["Embedded Intelligence, DFKI and TU Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"Embedded Intelligence, DFKI and TU Kaiserslautern, Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075317929"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13191791,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9997000098228455,"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":0.9997000098228455,"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/T10679","display_name":"Service-Oriented Architecture and Web Services","score":0.995199978351593,"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/T10028","display_name":"Topic Modeling","score":0.9927999973297119,"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":0.7249014377593994},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.6255332231521606},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5121033191680908},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09339150786399841},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.07240942120552063}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7249014377593994},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.6255332231521606},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5121033191680908},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09339150786399841},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.07240942120552063},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/percomworkshops48775.2020.9156117","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percomworkshops48775.2020.9156117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W157776793","https://openalex.org/W562299675","https://openalex.org/W1493490255","https://openalex.org/W1499578805","https://openalex.org/W1529731474","https://openalex.org/W1607983314","https://openalex.org/W1978762809","https://openalex.org/W1991570251","https://openalex.org/W2127978399","https://openalex.org/W2143017621","https://openalex.org/W2150650678","https://openalex.org/W2162340487","https://openalex.org/W2387719207","https://openalex.org/W2736191430","https://openalex.org/W2768942284","https://openalex.org/W2778385450","https://openalex.org/W2793340237","https://openalex.org/W2953320089","https://openalex.org/W2966776842","https://openalex.org/W6606440923","https://openalex.org/W6629638141","https://openalex.org/W6741853903"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"A":[0,35],"still":[1,101,144],"unsolved":[2],"issue":[3],"in":[4,13,25,49,172],"human":[5],"activity":[6],"recognition,":[7],"as":[8],"it":[9],"is":[10,17,41,191],"being":[11],"used":[12],"many":[14],"smart":[15],"systems,":[16],"the":[18,60,64,103,133,153,162,194,197],"availability":[19],"of":[20,66,135,187,196],"labeled":[21],"training":[22],"data":[23],"or,":[24],"other":[26],"words,":[27],"information":[28,62,95,158],"about":[29],"what":[30],"sensors":[31],"actually":[32],"are":[33,175],"recording.":[34],"possible":[36],"solution":[37],"to":[38,42,72,76,82,92,105,112,156,193],"this":[39,99,149,173,188],"problem":[40],"build":[43,83],"detailed":[44],"semantic":[45],"domain":[46,79,85],"models":[47,56],"specifying,":[48],"different":[50,118,121],"detail,":[51],"complex":[52],"compound":[53],"activities.":[54],"Such":[55],"would":[57],"allow":[58],"retrieving":[59],"required":[61],"without":[63],"necessity":[65,134],"time-consuming":[67],"labeling.":[68],"On":[69],"our":[70],"way":[71],"develop":[73],"a":[74,90,129],"method":[75,91,100,174,190,200],"leverage":[77],"text-based":[78],"descriptions":[80],"automatically":[81],"such":[84],"models,":[86],"we":[87,151],"previously":[88],"introduced":[89],"extract":[93],"domain-relevant":[94],"from":[96],"texts.":[97],"However,":[98],"included":[102],"requirement":[104],"hand-craft":[106],"socalled":[107],"\u201cregular":[108],"expressions,\u201d":[109],"which":[110],"have":[111],"be":[113],"adjusted":[114],"or":[115,131],"rebuilt":[116],"for":[117,128],"languages":[119],"and":[120,179,205],"styles.":[122],"Even":[123],"though":[124],"only":[125],"done":[126],"once":[127],"language":[130],"style,":[132],"handcrafting":[136],"regular":[137,163,198],"expressions":[138,164,199],"(and":[139],"their":[140],"fine-tuning":[141],"by":[142,160],"experts)":[143],"requires":[145],"extensive":[146],"work.":[147],"In":[148],"paper,":[150],"present":[152],"next":[154],"step":[155],"automatize":[157],"extraction":[159],"substituting":[161],"with":[165],"an":[166],"automated":[167],"neural":[168],"network.":[169],"All":[170],"steps":[171],"now":[176],"fully":[177],"automatic":[178],"do":[180],"not":[181],"require":[182],"any":[183],"hand-crafting.":[184],"The":[185],"performance":[186,195],"new":[189],"equal":[192],"before":[201],"(70":[202],"%":[203],"precision":[204],"recall).":[206]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}