{"id":"https://openalex.org/W7155604716","doi":"https://doi.org/10.1145/3789418.3789428","title":"Domain-Specific Knowledge Graph Construction via Reinforcement Learning-Augmented Large Language Models","display_name":"Domain-Specific Knowledge Graph Construction via Reinforcement Learning-Augmented Large Language Models","publication_year":2025,"publication_date":"2025-12-12","ids":{"openalex":"https://openalex.org/W7155604716","doi":"https://doi.org/10.1145/3789418.3789428"},"language":null,"primary_location":{"id":"doi:10.1145/3789418.3789428","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3789418.3789428","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Algorithms, Computing and Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3789418.3789428","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100299579","display_name":"Jiang Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Yang","raw_affiliation_strings":["Zhejiang Lab, HangZhou, China,"],"raw_orcid":"https://orcid.org/0009-0005-7569-9965","affiliations":[{"raw_affiliation_string":"Zhejiang Lab, HangZhou, China,","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134570464","display_name":"Yao Qi","orcid":"https://orcid.org/0009-0007-8840-039X"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Qi","raw_affiliation_strings":["Zhejiang Lab, HangZhou, China,"],"raw_orcid":"https://orcid.org/0009-0007-8840-039X","affiliations":[{"raw_affiliation_string":"Zhejiang Lab, HangZhou, China,","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134614186","display_name":"Wentao Yang","orcid":"https://orcid.org/0009-0001-0028-2795"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Yang","raw_affiliation_strings":["Zhejiang Lab, HangZhou, China,"],"raw_orcid":"https://orcid.org/0009-0001-0028-2795","affiliations":[{"raw_affiliation_string":"Zhejiang Lab, HangZhou, China,","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102019380","display_name":"Ting Jiang","orcid":"https://orcid.org/0000-0003-4925-2033"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Jiang","raw_affiliation_strings":["Zhejiang Lab, HangZhou, China,"],"raw_orcid":"https://orcid.org/0000-0003-4925-2033","affiliations":[{"raw_affiliation_string":"Zhejiang Lab, HangZhou, China,","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134574574","display_name":"Ziqi Song","orcid":"https://orcid.org/0009-0002-9618-1354"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqi Song","raw_affiliation_strings":["Zhejiang Lab, HangZhou, China,"],"raw_orcid":"https://orcid.org/0009-0002-9618-1354","affiliations":[{"raw_affiliation_string":"Zhejiang Lab, HangZhou, China,","institution_ids":["https://openalex.org/I4210123185"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210123185"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"79","last_page":"85"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.8337000012397766,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.8337000012397766,"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/T10028","display_name":"Topic Modeling","score":0.09939999878406525,"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":0.01269999984651804,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7376999855041504},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5406000018119812},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.48570001125335693},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4571000039577484},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.44040000438690186},{"id":"https://openalex.org/keywords/ontology","display_name":"Ontology","score":0.41449999809265137},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.4083000123500824},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.3801000118255615}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7664999961853027},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7376999855041504},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5406000018119812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5016999840736389},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.44040000438690186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4388999938964844},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3528999984264374},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C56289545","wikidata":"https://www.wikidata.org/wiki/Q6423376","display_name":"Knowledge integration","level":3,"score":0.33320000767707825},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28349998593330383},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.28049999475479126},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.262800008058548},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3789418.3789428","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3789418.3789428","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Algorithms, Computing and Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3789418.3789428","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3789418.3789428","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Algorithms, Computing and Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2738442461","https://openalex.org/W2951525151","https://openalex.org/W2963718112","https://openalex.org/W3002924435","https://openalex.org/W3214342214","https://openalex.org/W4214604362","https://openalex.org/W4302011088","https://openalex.org/W4377865170","https://openalex.org/W4381573861","https://openalex.org/W4385644386","https://openalex.org/W4386270350","https://openalex.org/W4387561375","https://openalex.org/W4389364456","https://openalex.org/W4389519012","https://openalex.org/W4389523710","https://openalex.org/W4390692489","https://openalex.org/W4394591548","https://openalex.org/W4395064786","https://openalex.org/W4396819101","https://openalex.org/W4403883376","https://openalex.org/W4404305096","https://openalex.org/W4405301224","https://openalex.org/W4405740130","https://openalex.org/W4405957503","https://openalex.org/W4406779522","https://openalex.org/W4407384832","https://openalex.org/W4407806427","https://openalex.org/W4409129276","https://openalex.org/W4416036779"],"related_works":[],"abstract_inverted_index":{"Although":[0],"the":[1,30,109,126],"integration":[2],"of":[3,32],"large":[4],"language":[5],"models":[6],"(LLMs)":[7],"has":[8],"significantly":[9],"advanced":[10],"knowledge":[11,133],"graph":[12,134],"(KG)":[13],"construction,":[14,56],"key":[15],"challenges":[16],"persist":[17],"in":[18],"their":[19],"application":[20],"to":[21,90],"specific":[22],"domains,":[23],"including":[24],"domain":[25],"misalignment,":[26],"hallucination-induced":[27],"errors,":[28],"and":[29,40,97,115,121,152],"constraints":[31],"static":[33],"methods":[34],"such":[35],"as":[36,87],"limited":[37],"context":[38],"windows":[39],"incomplete":[41],"schemas.":[42],"This":[43],"study":[44],"introduces":[45],"a":[46,67,83,103,146],"reinforcement":[47],"learning":[48],"(RL)-augmented":[49],"LLM":[50],"framework":[51,127],"designed":[52],"for":[53,93,149],"domain-specific":[54,116],"KG":[55,86,106,154],"which":[57],"unifies":[58],"LLM-based":[59],"extraction":[60,77],"with":[61,78],"RL-driven":[62],"adaptive":[63,79],"optimization.":[64],"We":[65],"propose":[66],"group":[68],"relative":[69],"policy":[70],"optimization":[71],"(GRPO)":[72],"method":[73],"that":[74,108,143],"integrates":[75],"generative":[76],"reward":[80],"shaping,":[81],"utilizing":[82],"curated":[84],"geoscience":[85],"reference":[88],"supervision":[89],"provide":[91],"rewards":[92],"factual":[94],"accuracy,":[95],"conciseness,":[96],"structural":[98],"validity.":[99],"Experiments":[100],"conducted":[101],"on":[102,131],"sedimentary":[104],"rock":[105],"demonstrate":[107],"RL-augmented":[110],"model":[111],"outperforms":[112],"both":[113],"general-purpose":[114],"baselines":[117],"by":[118],"reducing":[119],"hallucinations":[120],"enhancing":[122],"ontology":[123],"alignment.":[124],"Furthermore,":[125],"attains":[128],"comparable":[129],"performance":[130],"general":[132],"benchmarks,":[135],"demonstrating":[136],"its":[137],"cross-domain":[138],"robustness.":[139],"These":[140],"findings":[141],"suggest":[142],"RL":[144],"is":[145],"promising":[147],"strategy":[148],"scalable,":[150],"reliable,":[151],"domain-sensitive":[153],"construction.":[155]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-04-26T00:00:00"}
