of Large Language Models, Gerhard Weikum https://videolectures.net/iswc2023_weikum_knowledge_graphs/ Semantic Web Research in the Age of Generative Artificial Intelligence, Deborah McGuinness https://videolectures.net/iswc2023_mc_guinness_web_research/ https://www.slideshare.net/deborahmcguinness/iswc2023mcguinnesstwc16x9finalshortpdf [Yiming 03] Yiming Tan, Dehai Min, Yu Li, Wenbo Li, Nan Hu, Yongrui Chen and Guilin Qi, Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of GPT family LLMs' Question Answering Performance, Proc. ISWC2023, Part I, pp. 348-367, Athens, Greece, Nov. 2023. [大山 04] 大山 陽和太, 知識グラフと大規模言語モデルのファクト情報に関する質問応答能力の比較, 大阪電気通信 大学情報通信工学部・情報工学科学・卒業論文,2024. [脇所 04] 脇所 昂輝, Wikidataを用いた一問一答問題に対する解答生成パターンの分析と評価, 大阪電気通信大学 情報通信工学部・情報工学科学・卒業論文,2024. [Nandana 03] Nandana Mihindukulasooriya, Sanju Tiwari, Carlos Enguix and Kusum Lata, Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text, Proc. ISWC2023, Part II, pp. 247-265, Athens, Greece, Nov. 2023. [Hamed 03] Hamed Babaei Giglou, Jennifer D'Souza and Soren Auer, LLMs4OL: Large Language Models? for Ontology Learning, Proc. ISWC2023, Part I, pp. 408-427, Athens, Greece, Nov. 2023. [堀田 04] 堀田将吾,ナレッジグラフ推論チャレンジ2023「推理小説部門」応募作品, https://challenge.knowledge- graph.jp/results/results2023.html, 2024. [鈴木 04] 鈴木陽太,ナレッジグラフ推論チャレンジ2023「一般部門」応募作品, https://challenge.knowledge- graph.jp/results/results2023.html, 2024. ISWCサーベイ会 https://www.sigswo.org/ISWC-Survey JSAI2024企画セッション-生成AI時代のナレッジグラフ https://challenge.knowledge-graph.jp/jsai2024/