{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:20:12Z","timestamp":1772911212762,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve this problem, we introduce the meta-learning setting, where a set of tasks are sampled on the existing KG to mimic the link prediction task on the emerging KG. Based on sampled tasks, we meta-train a graph neural network framework that can construct features for unseen components based on structural information and output embeddings for them. Experimental results show that our proposed method can effectively embed unseen components and outperforms models that consider inductive settings for KGs and baselines that directly use conventional KG embedding methods.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/273","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"1966-1972","source":"Crossref","is-referenced-by-count":19,"title":["Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting"],"prefix":"10.24963","author":[{"given":"Mingyang","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University"}]},{"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software Technology, Zhejiang University"}]},{"given":"Zhen","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Software Technology, Zhejiang University"}]},{"given":"Xiangnan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Technology, Zhejiang University"}]},{"given":"Mengxiao","family":"Ding","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd."}]},{"given":"Fei","family":"Huang","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Huajun","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University"},{"name":"ZJU-Hangzhou Global Scientific and Technological Innovation Center"},{"name":"Alibaba-Zhejiang University Joint Institute of Frontier Technologies"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:08:44Z","timestamp":1658142524000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/273"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/273","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}