{"links":{},"id":2007720,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02007720","sets":["1164:1384:1771205742511:1771205807242"]},"path":["1771205807242"],"owner":"80578","recid":"2007720","title":["Structure-Aware Enhanced LLMs via Knowledge Graphs for Microservice Architecture Documentation"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2026-03-02"},"_buckets":{"deposit":"f436fa78-dac6-47d1-bb57-0b336db4c48f"},"_deposit":{"id":"2007720","pid":{"type":"depid","value":"2007720","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"Structure-Aware Enhanced LLMs via Knowledge Graphs for Microservice Architecture Documentation","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Structure-Aware Enhanced LLMs via Knowledge Graphs for Microservice Architecture Documentation","subitem_title_language":"ja"},{"subitem_title":"Structure-Aware Enhanced LLMs via Knowledge Graphs for Microservice Architecture Documentation","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2026-03-02","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"School of Computing, Institute of Science Tokyo"},{"subitem_text_value":"School of Computing, Institute of Science Tokyo"},{"subitem_text_value":"School of Computing, Institute of Science Tokyo"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"School of Computing, Institute of Science Tokyo","subitem_text_language":"en"},{"subitem_text_value":"School of Computing, Institute of Science Tokyo","subitem_text_language":"en"},{"subitem_text_value":"School of Computing, Institute of Science Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/2007720/files/IPSJ-SE26222020.pdf","label":"IPSJ-SE26222020.pdf"},"date":[{"dateType":"Available","dateValue":"2028-03-02"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SE26222020.pdf","filesize":[{"value":"938.0 KB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"12"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"7e0a4814-fe18-425d-b5aa-58f94a87b4ad","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2026 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Qiao,Lin"}]},{"creatorNames":[{"creatorName":"Profir-petru,Pârţachi"}]},{"creatorNames":[{"creatorName":"Takashi,Kobayashi"}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Qiao Lin","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Profir-petru Pârţachi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Takashi Kobayashi","creatorNameLang":"en"}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10112981","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8825","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Microservice documentation is critical yet hard to maintain due to its complex and distributed architecture. Developers use Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) to automate the creation and update of microservice documentations; however, this approach has limitations. Standard RAG is blind to the high level, global structural dependencies among services and relies solely on semantic similarity (Structural Blindness). As a result, LLMs suffer from missing key dependencies and information during generation and leading to inaccurate documentation. We introduce a Graph RAG framework that integrates structural awareness into the generation process. A Knowledge Graph (KG) and a GNN are used to help retrieve the most relevant dependencies for the LLM during generation. This ensures the generated documentation is both semantically coherent and architecturally complete, addressing some of the current limitation. As the result, we improve on three metrics over our baselines: Correctness (0.66-1.64), Completeness (0.86-2.24), and Faithfulness (0.64-1.62).","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Microservice documentation is critical yet hard to maintain due to its complex and distributed architecture. Developers use Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) to automate the creation and update of microservice documentations; however, this approach has limitations. Standard RAG is blind to the high level, global structural dependencies among services and relies solely on semantic similarity (Structural Blindness). As a result, LLMs suffer from missing key dependencies and information during generation and leading to inaccurate documentation. We introduce a Graph RAG framework that integrates structural awareness into the generation process. A Knowledge Graph (KG) and a GNN are used to help retrieve the most relevant dependencies for the LLM during generation. This ensures the generated documentation is both semantically coherent and architecturally complete, addressing some of the current limitation. As the result, we improve on three metrics over our baselines: Correctness (0.66-1.64), Completeness (0.86-2.24), and Faithfulness (0.64-1.62).","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ソフトウェア工学（SE）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2026-03-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"20","bibliographicVolumeNumber":"2026-SE-222"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"created":"2026-02-19T09:54:19.480892+00:00","updated":"2026-02-19T09:54:23.430160+00:00"}