{"id":"https://openalex.org/W4409634630","doi":"https://doi.org/10.3390/make7020039","title":"PIDQA\u2014Question Answering on Piping and Instrumentation Diagrams","display_name":"PIDQA\u2014Question Answering on Piping and Instrumentation Diagrams","publication_year":2025,"publication_date":"2025-04-21","ids":{"openalex":"https://openalex.org/W4409634630","doi":"https://doi.org/10.3390/make7020039"},"language":"en","primary_location":{"id":"doi:10.3390/make7020039","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020039","pdf_url":"https://www.mdpi.com/2504-4990/7/2/39/pdf?version=1745229630","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/2/39/pdf?version=1745229630","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100622860","display_name":"Mohit Gupta","orcid":"https://orcid.org/0000-0002-0677-4811"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mohit Gupta","raw_affiliation_strings":["School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287-1404, USA"],"raw_orcid":"https://orcid.org/0000-0002-0677-4811","affiliations":[{"raw_affiliation_string":"School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287-1404, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003857037","display_name":"Chialing Wei","orcid":"https://orcid.org/0000-0001-8191-9091"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chialing Wei","raw_affiliation_strings":["School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287-1404, USA"],"raw_orcid":"https://orcid.org/0000-0001-8191-9091","affiliations":[{"raw_affiliation_string":"School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287-1404, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075174290","display_name":"Thomas Czerniawski","orcid":"https://orcid.org/0000-0002-7310-6522"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas Czerniawski","raw_affiliation_strings":["School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287-1404, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287-1404, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071172386","display_name":"Ricardo Eiris","orcid":"https://orcid.org/0000-0001-6904-5352"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ricardo Eiris","raw_affiliation_strings":["School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287-1404, USA"],"raw_orcid":"https://orcid.org/0000-0001-6904-5352","affiliations":[{"raw_affiliation_string":"School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287-1404, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100622860"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":3.8114,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.9279508,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"7","issue":"2","first_page":"39","last_page":"39"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9894999861717224,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9894999861717224,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9871000051498413,"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/piping","display_name":"Piping","score":0.8507586121559143},{"id":"https://openalex.org/keywords/instrumentation","display_name":"Instrumentation (computer programming)","score":0.7511866092681885},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5873829126358032},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5016329288482666},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.42219892144203186},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.36395108699798584},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3098565936088562},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2719973623752594},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.1802353858947754}],"concepts":[{"id":"https://openalex.org/C2779095084","wikidata":"https://www.wikidata.org/wiki/Q3679502","display_name":"Piping","level":2,"score":0.8507586121559143},{"id":"https://openalex.org/C118530786","wikidata":"https://www.wikidata.org/wiki/Q1134732","display_name":"Instrumentation (computer programming)","level":2,"score":0.7511866092681885},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5873829126358032},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5016329288482666},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.42219892144203186},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.36395108699798584},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3098565936088562},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2719973623752594},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.1802353858947754}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7020039","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020039","pdf_url":"https://www.mdpi.com/2504-4990/7/2/39/pdf?version=1745229630","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3384a1280c04463bbaa3e5fe7b757bff","is_oa":true,"landing_page_url":"https://doaj.org/article/3384a1280c04463bbaa3e5fe7b757bff","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":"Machine Learning and Knowledge Extraction, Vol 7, Iss 2, p 39 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7020039","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020039","pdf_url":"https://www.mdpi.com/2504-4990/7/2/39/pdf?version=1745229630","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4409634630.pdf"},"referenced_works_count":63,"referenced_works":["https://openalex.org/W1933349210","https://openalex.org/W1984660414","https://openalex.org/W2001642682","https://openalex.org/W2008102742","https://openalex.org/W2030432398","https://openalex.org/W2079304821","https://openalex.org/W2102605133","https://openalex.org/W2132022337","https://openalex.org/W2132679783","https://openalex.org/W2181299418","https://openalex.org/W2189149111","https://openalex.org/W2555498673","https://openalex.org/W2597425697","https://openalex.org/W2759136286","https://openalex.org/W2800773563","https://openalex.org/W2807421490","https://openalex.org/W2911500381","https://openalex.org/W2946108305","https://openalex.org/W2967615747","https://openalex.org/W2991098024","https://openalex.org/W2999758404","https://openalex.org/W3030163527","https://openalex.org/W3093819145","https://openalex.org/W3102624033","https://openalex.org/W3159019485","https://openalex.org/W3170068246","https://openalex.org/W3172610831","https://openalex.org/W3175818566","https://openalex.org/W3185341429","https://openalex.org/W3191263508","https://openalex.org/W3199394762","https://openalex.org/W3209816613","https://openalex.org/W3214685499","https://openalex.org/W4221143196","https://openalex.org/W4226182655","https://openalex.org/W4226271749","https://openalex.org/W4281624973","https://openalex.org/W4283155341","https://openalex.org/W4292779060","https://openalex.org/W4294326460","https://openalex.org/W4296605665","https://openalex.org/W4302764050","https://openalex.org/W4307411363","https://openalex.org/W4316659141","https://openalex.org/W4321101504","https://openalex.org/W4381326864","https://openalex.org/W4384642746","https://openalex.org/W4385284779","https://openalex.org/W4385553889","https://openalex.org/W4386051124","https://openalex.org/W4389550712","https://openalex.org/W4390597978","https://openalex.org/W4394597438","https://openalex.org/W4402029545","https://openalex.org/W4402835407","https://openalex.org/W4403081594","https://openalex.org/W4405272469","https://openalex.org/W6622621606","https://openalex.org/W6798057236","https://openalex.org/W6838480431","https://openalex.org/W6849757037","https://openalex.org/W6853547958","https://openalex.org/W7058345372"],"related_works":["https://openalex.org/W2642647287","https://openalex.org/W415128147","https://openalex.org/W2460600108","https://openalex.org/W2092522664","https://openalex.org/W2614019867","https://openalex.org/W2993874308","https://openalex.org/W2363498374","https://openalex.org/W2152596889","https://openalex.org/W2489600020","https://openalex.org/W1497201623"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,18,39,47,56,67,81,90,94,113],"novel":[4,121],"framework":[5],"enabling":[6],"natural":[7],"language":[8,97],"question":[9],"answering":[10],"on":[11,106],"Piping":[12],"and":[13,25,50,78,99,141,194],"Instrumentation":[14],"Diagrams":[15],"(P&IDs),":[16],"addressing":[17],"critical":[19],"gap":[20],"between":[21],"engineering":[22],"design":[23,192],"documentation":[24],"intuitive":[26],"information":[27,86],"retrieval.":[28],"Our":[29,145],"approach":[30],"transforms":[31],"static":[32],"P&IDs":[33],"into":[34,66,93],"queryable":[35],"knowledge":[36],"bases":[37],"through":[38],"three-stage":[40],"pipeline.":[41],"First,":[42],"we":[43,111],"recognize":[44],"entities":[45],"in":[46,179,191],"P&ID":[48,116,181],"image":[49,117],"organize":[51],"their":[52],"relationships":[53],"to":[54],"form":[55],"base":[57],"entity":[58,62],"graph.":[59],"Second,":[60],"this":[61,183],"graph":[63,95],"is":[64],"converted":[65],"Labeled":[68],"Property":[69],"Graph":[70],"(LPG),":[71],"enriched":[72],"with":[73,119,154],"semantic":[74],"attributes":[75],"for":[76,188],"nodes":[77],"edges.":[79],"Third,":[80],"Large":[82],"Language":[83],"Model":[84],"(LLM)-based":[85],"retrieval":[87],"system":[88],"translates":[89],"user":[91],"query":[92,96],"(Cypher)":[98],"retrieves":[100],"the":[101,152],"answer":[102],"by":[103,161],"executing":[104],"it":[105],"LPG.":[107],"For":[108],"our":[109,120],"experiments,":[110],"augmented":[112],"publicly":[114],"available":[115],"dataset":[118],"PIDQA":[122],"dataset,":[123],"which":[124],"comprises":[125],"64,000":[126],"question\u2013answer":[127],"pairs":[128],"spanning":[129],"four":[130],"categories:":[131],"(I)":[132],"simple":[133],"counting,":[134,137],"(II)":[135],"spatial":[136,139],"(III)":[138],"connections,":[140],"(IV)":[142],"value-based":[143],"questions.":[144],"experiments":[146],"(using":[147],"gpt-3.5-turbo)":[148],"demonstrate":[149],"that":[150],"grounding":[151],"LLM":[153],"dynamic":[155],"few-shot":[156],"sampling":[157],"robustly":[158],"elevates":[159],"accuracy":[160],"10.6\u201343.5%":[162],"over":[163],"schema":[164],"contextualization":[165],"alone,":[166],"even":[167],"under":[168],"high":[169],"lexical":[170],"diversity":[171],"conditions":[172],"(e.g.,":[173],"paraphrasing,":[174],"ambiguity).":[175],"By":[176],"reducing":[177],"barriers":[178],"retrieving":[180],"data,":[182],"work":[184],"advances":[185],"human\u2013AI":[186],"collaboration":[187],"industrial":[189],"workflows":[190],"validation":[193],"safety":[195],"audits.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}