{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:08:02Z","timestamp":1760148482151,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Water Resources Science and Technology Projects in Jiangsu Province","award":["2017065","2018YFC0407106"],"award-info":[{"award-number":["2017065","2018YFC0407106"]}]},{"name":"National Key R &amp; D Program of China","award":["2017065","2018YFC0407106"],"award-info":[{"award-number":["2017065","2018YFC0407106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The purpose of knowledge representation learning is to learn the vector representation of research objects projected by a matrix in low-dimensional vector space and explore the relationship between embedded objects in low-dimensional space. However, most methods only consider the triple structure in the knowledge graph and ignore the additional information related to the triple, especially the text description information. In this paper, we propose a knowledge graph representation model with a symmetric architecture called Joint Knowledge Representation Learning of Text Description and Knowledge Graph (JKRL), which models the entity description and relationship description of the triple structure for joint representation learning of knowledge and balances the contribution of the triple structure and text description in the process of vector learning. First, we adopt the TransE model to learn the structural vector representations of entities and relations, and then use a CNN model to encode the entity description to obtain the text representation of the entity. To semantically encode the relation descriptions, we designed an Attention-Bi-LSTM text encoder, which introduces an attention mechanism into the Bi-LSTM model to calculate the semantic relevance between each word in the sentence and different relations. In addition, we also introduce position features into word features in order to better encode word order information. Finally, we define a joint evaluation function to learn the joint representation of structural and textual representations. The experiments show that compared with the baseline methods, our model achieves the best performance on both Mean Rank and Hits@10 metrics. The accuracy of the triple classification task on the FB15K dataset reached 93.2%.<\/jats:p>","DOI":"10.3390\/sym15051056","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T01:37:09Z","timestamp":1683769029000},"page":"1056","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["JKRL: Joint Knowledge Representation Learning of Text Description and Knowledge Graph"],"prefix":"10.3390","volume":"15","author":[{"given":"Guoyan","family":"Xu","sequence":"first","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qirui","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Du","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijun","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuwei","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.neucom.2020.12.012","article-title":"Enhancing knowledge graph embedding with relational constraints","volume":"429","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.neucom.2020.07.137","article-title":"Recalibration convolutional networks for learning interaction knowledge graph embedding","volume":"427","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100174","DOI":"10.1016\/j.bdr.2020.100174","article-title":"SMR: Medical knowledge graph embedding for safe medicine recommendation","volume":"23","author":"Gong","year":"2021","journal-title":"Big Data Res."},{"key":"ref_4","first-page":"31","article-title":"A Comprehensive Survey of Knowledge Graph Embeddings with Literals: Techniques and Applications","volume":"2377","author":"Gesese","year":"2019","journal-title":"DL4KG@ ESWC"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, M., Qiu, L., and Wang, X. (2021). A survey on knowledge graph embeddings for link prediction. Symmetry, 13.","DOI":"10.3390\/sym13030485"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ferrari, I., Frisoni, G., Italiani, P., Moro, G., and Sartori, C. (2022). Comprehensive Analysis of Knowledge Graph Embedding Techniques Benchmarked on Link Prediction. Electronics, 11.","DOI":"10.3390\/electronics11233866"},{"key":"ref_7","first-page":"589","article-title":"Review on knowledge graph techniques","volume":"45","author":"Xu","year":"2016","journal-title":"J. Univ. Electron. Sci. Technol. China"},{"key":"ref_8","first-page":"2048","article-title":"Knowledge graph embedding technology: A review","volume":"15","author":"Shu","year":"2021","journal-title":"J. Front. Comput. Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xie, Q., Ma, X., Dai, Z., and Hovy, E. (2017). An interpretable knowledge transfer model for knowledge base completion. arXiv.","DOI":"10.18653\/v1\/P17-1088"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shi, B., and Weninger, T. (2017, January 4\u20139). Proje: Embedding projection for knowledge graph completion. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10677"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Frisoni, G., Moro, G., Carlassare, G., and Carbonaro, A. (2021). Unsupervised event graph representation and similarity learning on biomedical literature. Sensors, 22.","DOI":"10.3390\/s22010003"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mintz, M., Bills, S., Snow, R., and Jurafsky, D. (2009, January 2\u20137). Distant supervision for relation extraction without labeled data. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Singapore.","DOI":"10.3115\/1690219.1690287"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xie, R., Liu, Z., Jia, J., Luan, H., and Sun, M. (2016, January 12\u201317). Representation learning of knowledge graphs with entity descriptions. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10329"},{"key":"ref_14","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_15","unstructured":"Han, X., Liu, Z., and Sun, M. (2016). Joint representation learning of text and knowledge for knowledge graph completion. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., and Chen, Z. (2014, January 25\u201329). Knowledge graph and text jointly embedding. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1167"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhong, H., Zhang, J., Wang, Z., Wan, H., and Chen, Z. (2015, January 17\u201321). Aligning knowledge and text embeddings by entity descriptions. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1031"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yuan, B., Wang, D., and Liu, R. (2015, January 26\u201331). Joint semantic relevance learning with text data and graph knowledge. Proceedings of the 3rd Workshop on Continuous Vector Space Models and Their Compositionality, Beijing, China.","DOI":"10.18653\/v1\/W15-4004"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"26466","DOI":"10.1109\/ACCESS.2019.2901544","article-title":"Representation learning of Knowledge Graphs via fine-grained relation description combinations","volume":"7","author":"He","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, J., Chen, K., Qiu, X., and Huang, X. (2016). Knowledge graph representation with jointly structural and textual encoding. arXiv.","DOI":"10.24963\/ijcai.2017\/183"},{"key":"ref_21","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_23","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O. (2013, January 5\u201310). Translating embeddings for modeling multi-relational data. Proceedings of the Advances in Neural Information Processing Systems 26 (NIPS 2013), Lake Tahoe, NA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., and Chen, Z. (2014, January 27\u201331). Knowledge graph embedding by translating on hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence, Qu\u00e9bec City, QC, Canada.","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., and Zhu, X. (2015, January 25\u201330). Learning entity and relation embeddings for knowledge graph completion. Proceedings of the AAAI Conference on Artificial Intelligence, Austin, TX, USA.","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ji, G., He, S., Xu, L., Liu, K., and Zhao, J. (2015, January 26\u201331). Knowledge graph embedding via dynamic mapping matrix. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China.","DOI":"10.3115\/v1\/P15-1067"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, S., Tian, J., Zhang, H., Yan, J., He, H., and Jin, Y. (2019, January 10\u201316). TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics. Proceedings of the IJCAI, Macao, China.","DOI":"10.24963\/ijcai.2019\/268"},{"key":"ref_28","unstructured":"Yang, B., Yih, W.-T., He, X., Gao, J., and Deng, L. (2014). Embedding entities and relations for learning and inference in knowledge bases. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nickel, M., Rosasco, L., and Poggio, T. (2016, January 12\u201317). Holographic embeddings of knowledge graphs. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10314"},{"key":"ref_30","unstructured":"Liu, H., Wu, Y., and Yang, Y. (2017, January 6\u201311). Analogical inference for multi-relational embeddings. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_31","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., and Bouchard, G. (2016, January 20\u201322). Complex embeddings for simple link prediction. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., and Riedel, S. (2018, January 2\u20137). Convolutional 2d knowledge graph embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., and Phung, D. (2017). A novel embedding model for knowledge base completion based on convolutional neural network. arXiv.","DOI":"10.18653\/v1\/N18-2053"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., and Liu, S. (2015). Modeling relation paths for representation learning of knowledge bases. arXiv.","DOI":"10.18653\/v1\/D15-1082"},{"key":"ref_35","unstructured":"Feng, J., Huang, M., Yang, Y., and Zhu, X. (2016, January 11\u201316). GAKE: Graph aware knowledge embedding. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan."},{"key":"ref_36","unstructured":"Wang, Z., Li, J., Liu, Z., and Tang, J. (2016, January 9\u201315). Text-enhanced representation learning for knowledge graph. Proceedings of the International joint conference on artificial intelligent (IJCAI), New York, NY, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"An, B., Chen, B., Han, X., and Sun, L. (2018, January 1\u20136). Accurate text-enhanced knowledge graph representation learning. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, LA, USA.","DOI":"10.18653\/v1\/N18-1068"},{"key":"ref_38","unstructured":"Yao, L., Mao, C., and Luo, Y. (2019). KG-BERT: BERT for knowledge graph completion. arXiv."},{"key":"ref_39","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, B., Shen, T., Long, G., Zhou, T., Wang, Y., and Chang, Y. (2021, January 19\u201323). Structure-augmented text representation learning for efficient knowledge graph completion. Proceedings of the Web Conference 2021, Virtual.","DOI":"10.1145\/3442381.3450043"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shen, J., Wang, C., Gong, L., and Song, D. (2022). Joint language semantic and structure embedding for knowledge graph completion. arXiv.","DOI":"10.1016\/j.knosys.2021.107963"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, M., Tian, Y., Chang, K.-W., Skiena, S., and Zaniolo, C. (2018). Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment. arXiv.","DOI":"10.24963\/ijcai.2018\/556"},{"key":"ref_43","unstructured":"Cochez, M., Garofalo, M., Len\u00dfen, J., and Pellegrino, M.A. (2018). A first experiment on including text literals in KGloVe. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wu, Y., and Wang, Z. (2018, January 20). Knowledge graph embedding with numeric attributes of entities. Proceedings of the Third Workshop on Representation Learning for NLP, Melbourne, Australia.","DOI":"10.18653\/v1\/W18-3017"},{"key":"ref_45","unstructured":"Trisedya, B.D., Qi, J., and Zhang, R. (February, January 27). Entity alignment between knowledge graphs using attribute embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pezeshkpour, P., Chen, L., and Singh, S. (2018). Embedding multimodal relational data for knowledge base completion. arXiv.","DOI":"10.18653\/v1\/D18-1359"},{"key":"ref_47","unstructured":"Xie, R., Liu, Z., and Sun, M. (2016, January 9\u201315). Representation learning of knowledge graphs with hierarchical types. Proceedings of the IJCAI, New York, NY, USA."},{"key":"ref_48","unstructured":"Esteban, C., Tresp, V., Yang, Y., Baier, S., and Krompa\u00df, D. (2016, January 5\u20138). Predicting the co-evolution of event and knowledge graphs. Proceedings of the 2016 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Guan, S., Jin, X., Wang, Y., and Cheng, X. (2019, January 13\u201317). Link prediction on n-ary relational data. Proceedings of the The World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313414"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Rosso, P., Yang, D., and Cudr\u00e9-Mauroux, P. (2020, January 20\u201324). Beyond triplets: Hyper-relational knowledge graph embedding for link prediction. Proceedings of the Web Conference 2020, Taipei, Taiwan.","DOI":"10.1145\/3366423.3380257"},{"key":"ref_51","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_52","unstructured":"Chen, Y. (2015). Convolutional Neural Network for Sentence Classification, University of Waterloo."},{"key":"ref_53","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5\u201310). Distributed representations of words and phrases and their compositionality. Proceedings of the Advances in Neural Information Processing Systems 26 (NIPS 2013), Lake Tahoe, NA, USA."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"117197","DOI":"10.1016\/j.energy.2020.117197","article-title":"LSTM based long-term energy consumption prediction with periodicity","volume":"197","author":"Wang","year":"2020","journal-title":"Energy"},{"key":"ref_55","unstructured":"Bollacker, K., Cook, R., and Tufts, P. (2007, January 22\u201326). Freebase: A shared database of structured general human knowledge. Proceedings of the AAAI, Vancouver, BC, Canada."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/5\/1056\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:32:15Z","timestamp":1760124735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/5\/1056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":55,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["sym15051056"],"URL":"https:\/\/doi.org\/10.3390\/sym15051056","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2023,5,10]]}}}