{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:00:41Z","timestamp":1777705241173,"version":"3.51.4"},"reference-count":9,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,1,30]]},"abstract":"<jats:p>\u00a0Knowledge Graph Embedding (KGE), which aims to embed the entities and relations of a knowledge gxraph into a low-dimensional continuous space, has been proven to be an effective method for completing a knowledge graph and improving the quality of the knowledge graph. The translation-based models represented by TransE, TransH, TransR and TransD have achieved great success in this regard. There is still potential for improvement in dealing with complex relations. In this paper, we find that the lack of flexibility in entity embedding limits the model\u2019s ability to model complex relations. Therefore, we propose single-directional-flexible (sdf) models and multi-directional-flexible (mdf) models to increase the flexibility and expressiveness of entity embeddings. These two methods can be applied to the TransD model and its variant models without increasing any time cost and space cost. We conduct experiments on benchmarks such as WN18 and FB15k. The experimental results show that the models significantly surpasses the classical translation models in both tasks of triplet classification and link prediction. In particular, for Hits@1 of link prediction of WN18, we get 71.7% after applying our method to TransD, which is much better than 24.1% of TransD.<\/jats:p>","DOI":"10.3233\/jifs-211553","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T11:26:43Z","timestamp":1650626803000},"page":"3093-3105","source":"Crossref","is-referenced-by-count":5,"title":["Two flexible translation-based models for knowledge graph embedding"],"prefix":"10.1177","volume":"44","author":[{"given":"Zepeng","family":"Li","sequence":"first","affiliation":[{"name":"Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rikui","family":"Huang","sequence":"additional","affiliation":[{"name":"Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianghong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Hu","sequence":"additional","affiliation":[{"name":"Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China"},{"name":"Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China"},{"name":"CAS Center for Excellence in Brain Science and Institutes for Biological Sciences, Shanghai Institutes for Biological Sciences, Chines Academy of Sciences, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211553_ref1","first-page":"4463","article-title":"Multi-relational poincar\u00e9 graph embeddings","volume":"32","author":"Balazevic","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"10.3233\/JIFS-211553_ref2","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s10994-013-5363-6","article-title":"A semantic matching energy function for learning with multi-relational data","volume":"94","author":"Bordes","year":"2014","journal-title":"Machine Learning"},{"key":"10.3233\/JIFS-211553_ref4","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.inffus.2020.01.008","article-title":"Feature-level fusion approaches based on multimodal EEG data for depression recognition","volume":"59","author":"Cai","year":"2020","journal-title":"Information Fusion"},{"key":"10.3233\/JIFS-211553_ref6","unstructured":"Dai Q.N. , Tu D.N. , Nguyen D.Q. and Phung D. , A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2 (Short Papers) (2018)."},{"key":"10.3233\/JIFS-211553_ref14","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.knosys.2018.03.020","article-title":"Path-specific knowledge graph embedding","volume":"151","author":"Jia","year":"2018","journal-title":"Knowledge-Based Systems"},{"issue":"12","key":"10.3233\/JIFS-211553_ref29","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","year":"2017","journal-title":"IEEE Transactions on Knowledge & Data Engineering"},{"key":"10.3233\/JIFS-211553_ref35","doi-asserted-by":"crossref","first-page":"106564","DOI":"10.1016\/j.knosys.2020.106564","article-title":"Knowledge graph embedding by translating in time domain space for link prediction","volume":"212","author":"Zhang","year":"2021","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"10.3233\/JIFS-211553_ref36","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s10844-019-00592-7","article-title":"Improve the translational distance models for knowledge graph embedding","volume":"55","author":"Zhang","year":"2020","journal-title":"Journal of Intelligent Information Systems"},{"issue":"3","key":"10.3233\/JIFS-211553_ref37","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1609\/aaai.v34i03.5701","article-title":"Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction","volume":"34","author":"Zhang","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-211553","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:18Z","timestamp":1777455798000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-211553"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":9,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-211553","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,30]]}}}