{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T10:27:59Z","timestamp":1773829679048,"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":[[2021,8]]},"abstract":"<jats:p>Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embedding-based models, and the conventional reasoning and lexical matching based systems. The former compute the similarity of entities via their cross-KG embeddings, but they usually rely on an ideal supervised learning setting for good performance and lack appropriate reasoning to avoid logically wrong mappings; while the latter address the reasoning issue but are poor at utilizing the KG graph structures and the entity contexts. In this study, we aim at combining the above two solutions and thus propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. It learns the KG embeddings via entity mappings from a probabilistic reasoning system named PARIS, and feeds the resultant entity mappings and embeddings back into PARIS for augmentation. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/278","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"2019-2025","source":"Crossref","is-referenced-by-count":23,"title":["Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding"],"prefix":"10.24963","author":[{"given":"Zhiyuan","family":"Qi","sequence":"first","affiliation":[{"name":"Tencent Jarvis Lab, Shenzhen, China"}]},{"given":"Ziheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tencent Jarvis Lab, Shenzhen, China"}]},{"given":"Jiaoyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oxford, UK"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"Tencent Jarvis Lab, Shenzhen, China"},{"name":"Platform and Content Group, Tencent, Shenzhen, China"}]},{"given":"Yuejia","family":"Xiang","sequence":"additional","affiliation":[{"name":"Tencent Jarvis Lab, Shenzhen, China"},{"name":"Platform and Content Group, Tencent, Shenzhen, China"}]},{"given":"Ningyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"given":"Yefeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tencent Jarvis Lab, Shenzhen, China"}]}],"member":"10584","event":{"name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2021","number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2021,8,19]]},"end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:02:23Z","timestamp":1628679743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/278"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/278","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}