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Under this framework, a H-AKRL (Hypergraph Neural Networks based Attribute-embodied Knowledge Representation Learning) model is put forward, where the hypergraph neural network is used to model the correlation between entities and attributes at a higher level. The complementary relationship between attribute information and structural information is taken full advantage of, enabling H-AKRL to finally achieve the goal of improving link prediction performance. Experiments on multiple real-world data sets show that the H-AKRL model has significantly improved the link prediction performance, especially in the embeddings of long tail entities.<\/jats:p>","DOI":"10.3233\/ida-216007","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T15:43:25Z","timestamp":1657640605000},"page":"959-975","source":"Crossref","is-referenced-by-count":6,"title":["Knowledge graph embedding with entity attributes using hypergraph neural networks"],"prefix":"10.1177","volume":"26","author":[{"given":"You-Wei","family":"Xu","sequence":"first","affiliation":[]},{"given":"Hong-Jun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Xiang-Lin","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Zi-Xuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yun-Bo","family":"Li","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/IDA-216007_ref2","doi-asserted-by":"crossref","first-page":"107637","DOI":"10.1016\/j.patcog.2020.107637","article-title":"Hypergraph convolution and hypergraph attention","volume":"110","author":"Bai","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.3233\/IDA-216007_ref3","doi-asserted-by":"crossref","unstructured":"I. 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