{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:20:44Z","timestamp":1699834844774},"reference-count":40,"publisher":"World Scientific Pub Co Pte Ltd","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2021,10]]},"abstract":"Knowledge representation learning (knowledge graph embedding) plays a critical role in the application of knowledge graph construction. The multi-source information knowledge representation learning, which is one class of the most promising knowledge representation learning at present, mainly focuses on learning a large number of useful additional information of entities and relations in the knowledge graph into their embeddings, such as the text description information, entity type information, visual information, graph structure information, etc. However, there is a kind of simple but very common information\u00a0\u2014 the number of an entity\u2019s relations which means the number of an entity\u2019s semantic types has been ignored. This work proposes a multi-source knowledge representation learning model KRL-NER, which embodies information of the number of an entity\u2019s relations between entities into the entities\u2019 embeddings through the attention mechanism. Specifically, first of all, we design and construct a submodel of the KRL-NER LearnNER which learns an embedding including the information on the number of an entity\u2019s relations; then, we obtain a new embedding by exerting attention onto the embedding learned by the models such as TransE with this embedding; finally, we translate based onto the new embedding. Experiments, such as related tasks on knowledge graph: entity prediction, entity prediction under different relation types, and triple classification, are carried out to verify our model. The results show that our model is effective on the large-scale knowledge graphs, e.g. FB15K.<\/jats:p>","DOI":"10.1142\/s0218194021500509","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T09:58:46Z","timestamp":1636970326000},"page":"1495-1515","source":"Crossref","is-referenced-by-count":1,"title":["Embodying the Number of an Entity\u2019s Relations for Knowledge Representation Learning"],"prefix":"10.1142","volume":"31","author":[{"given":"Xinhua","family":"Suo","sequence":"first","affiliation":[{"name":"School of Computer Science & Engineering, SiChuan University, No. 24, South Section 1, Yihuan Road, ChengDu, SiChuan 610065, P.\u00a0R.\u00a0China"}]},{"given":"Bing","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science & Engineering, SiChuan University, No. 24, South Section 1, Yihuan Road, ChengDu, SiChuan 610065, P.\u00a0R.\u00a0China"}]},{"given":"Yan","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu University of Information Technology, No. 24, Section 1, XueFu Road, Southwest Airport Economic Development Zone, Chengdu, Sichuan 610225, P.\u00a0R.\u00a0China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science & Engineering, SiChuan University, No. 24, South Section 1, Yihuan Road, ChengDu, SiChuan 610065, P.\u00a0R.\u00a0China"}]},{"given":"Yaosen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science & Engineering, SiChuan University, No. 24, South Section 1, Yihuan Road, ChengDu, SiChuan 610065, P.\u00a0R.\u00a0China"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science & Engineering, SiChuan University, No. 24, South Section 1, Yihuan Road, ChengDu, SiChuan 610065, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"S0218194021500509BIB001","author":"Singhal A.","year":"2012","journal-title":"Official Google Blog"},{"key":"S0218194021500509BIB002","first-page":"1","author":"Ji S.","year":"2021","journal-title":"IEEE Trans. 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