{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T01:33:08Z","timestamp":1777599188402,"version":"3.51.4"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,19]],"date-time":"2022-02-19T00:00:00Z","timestamp":1645228800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Entity alignment is the task of integrating heterogeneous knowledge among different knowledge graphs (KGs). KG is a popular way of storing facts about real-world entities. Unfortunately, a very limited number of the entities stored in different KGs are aligned. This paper presents an embedding-based entity alignment method that finds entity alignment by measuring the similarities between entity embeddings. Existing methods mainly focus on the relational structures and attributes information for the alignment process. Such methods fail while the entities have a fewer number of attributes or when the relational structure could not capture the meaningful representation of the entities. To address this problem, we propose EASAE, an entity alignment method using summary and attribute embeddings. We exploit the entity summary information available in KGs for entities\u2019 summary embedding. To learn the semantics of the entity summary, we employ bidirectional encoder representations from transformers (BERT). Our model learns the representations of entities by using relational triples, attribute triples and summary as well. We perform experiments on real-world datasets, and the results indicate that the proposed approach outperformed the state-of-the-art models for entity alignment.<\/jats:p>","DOI":"10.1093\/jigpal\/jzac021","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T20:09:09Z","timestamp":1643486949000},"page":"314-324","source":"Crossref","is-referenced-by-count":14,"title":["Entity alignment via summary and attribute embeddings"],"prefix":"10.1093","volume":"31","author":[{"given":"Rumana Ferdous","family":"Munne","sequence":"first","affiliation":[{"name":"SOKENDAI (The Graduate University for Advanced Studies) , National Institute of Informatics, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryutaro","family":"Ichise","sequence":"additional","affiliation":[{"name":"National Institute of Informatics, Tokyo, Japan , SOKENDAI (The Graduate University for Advanced Studies)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"2023033115514028900_","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","volume-title":"Proceedings of NIPS","author":"Bordes","year":"2013"},{"key":"2023033115514028900_","first-page":"3998","article-title":"Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment","volume-title":"Proceedings of IJCAI","author":"Chen","year":"2018"},{"key":"2023033115514028900_","first-page":"5169","article-title":"Learning multi-faceted knowledge graph embeddings for natural language processing","volume-title":"Proceedings of IJCAI","author":"Chen","year":"2017"},{"key":"2023033115514028900_","first-page":"4171","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of NAACL","author":"Devlin","year":"2019"},{"key":"2023033115514028900_","first-page":"3","article-title":"A joint embedding method for entity alignment of knowledge bases","volume-title":"Proceedings of CCKS","author":"Hao","year":"2016"},{"key":"2023033115514028900_","doi-asserted-by":"crossref","first-page":"167","DOI":"10.3233\/SW-140134","article-title":"Dbpedia\u2014a large-scale, multilingual knowledge base extracted from wikipedia","volume":"6","author":"Lehmann","year":"2015","journal-title":"Semantic Web"},{"key":"2023033115514028900_","first-page":"107","article-title":"Joint entity summary and attribute embeddings for entity alignment between knowledge graphs","volume-title":"Proceedings of HAIS","author":"Munne","year":"2020"},{"key":"2023033115514028900_","first-page":"697","article-title":"Yago: a core of semantic knowledge","volume-title":"Proceedings of WWW","author":"Suchanek","year":"2007"},{"key":"2023033115514028900_","first-page":"628","article-title":"Cross-lingual entity alignment via joint attribute-preserving embedding","volume-title":"Proceedings of ISWC","author":"Sun","year":"2017"},{"key":"2023033115514028900_","first-page":"4396","article-title":"Bootstrapping entity alignment with knowledge graph embedding","volume-title":"Proceedings of IJCAI","author":"Sun","year":"2018"},{"key":"2023033115514028900_","first-page":"3174","article-title":"Bert-int: A bert-based interaction model for knowledge graph alignment","volume-title":"Proceedings of IJCAI","author":"Tang","year":"2020"},{"key":"2023033115514028900_","first-page":"297","article-title":"Entity alignment between knowledge graphs using attribute embeddings","volume-title":"Proceedings of AAAI","author":"Trisedya","year":"2019"},{"key":"2023033115514028900_","doi-asserted-by":"crossref","DOI":"10.1145\/2629489","article-title":"Wikidata","volume-title":"Commun. 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