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Recently, embedding-based methods calculate the similarity between entity pairs through vector embeddings and thus can deal with different knowledge graphs. However, embedding-based methods mostly require humans to annotate data, which is laborious. Therefore, we learn from each other to propose an unsupervised entity alignment framework in this work, which can generate initial alignment seeds automatically by considering symbolic similarities. It can effectively avoid the waste of human resources and is suitable for handling multiple types of knowledge graphs. In addition, we investigate improving the quality and quantity of initial alignment by integrating multiple symbolic similarity features of entities and dealing with the situation of entity information missing better. Experimental results on 3 real datasets demonstrate its state-of-the-art performance.<\/jats:p>","DOI":"10.34133\/icomputing.0021","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T06:48:40Z","timestamp":1680677320000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":2,"title":["Integrating Symbol Similarities with Knowledge Graph Embedding for Entity Alignment: An Unsupervised Framework"],"prefix":"10.34133","volume":"2","author":[{"given":"Tingting","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Information and Computer, Anhui Agricultural University, Hefei, China."},{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, China."},{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Bu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, China."},{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, China."},{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China."},{"name":"School of Information Engineering, Yangzhou University, Yangzhou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, China."},{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China."},{"name":"Research Center for Knowledge Engineering, Zhejiang Lab, Hangzhou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"issue":"12","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","article-title":"Knowledge graph embedding: A survey of approaches and applications","volume":"29","author":"Wang Q","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L. 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