{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:31:39Z","timestamp":1772119899098,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11280-024-01314-y","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T04:29:46Z","timestamp":1734496186000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Incorporating structural knowledge into language models for open knowledge graph completion"],"prefix":"10.1007","volume":"28","author":[{"given":"Xin","family":"Song","sequence":"first","affiliation":[]},{"given":"Ye","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Haiyang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yanyi","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Liqun","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"1314_CR1","doi-asserted-by":"crossref","unstructured":"Sun, H., Bedrax-Weiss, T., Cohen, W.W.: Pullnet: open domain question answering with iterative retrieval on knowledge bases and text. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) EMNLP-IJCNLP, pp. 2380\u20132390 (2019)","DOI":"10.18653\/v1\/D19-1242"},{"key":"1314_CR2","doi-asserted-by":"crossref","unstructured":"Huang, J., Zhao, W.X., Dou, H., Wen, J., Chang, E.Y.: Improving sequential recommendation with knowledge-enhanced memory networks. In: Collins-Thompson, K., Mei, Q., Davison, B.D., Liu, Y., Yilmaz, E. (eds.) The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR, pp. 505\u2013514 (2018)","DOI":"10.1145\/3209978.3210017"},{"key":"1314_CR3","unstructured":"Bordes, A., Usunier, N., Garc\u00eda-Dur\u00e1n, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013., pp. 2787\u20132795 (2013)"},{"key":"1314_CR4","unstructured":"Sun, Z., Deng, Z., Nie, J., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. In: 7th International Conference on Learning Representations, ICLR, New Orleans, LA, USA, May 6-9, 2019 (2019)"},{"key":"1314_CR5","unstructured":"Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.: Composition-based multi-relational graph convolutional networks. (2019)"},{"key":"1314_CR6","doi-asserted-by":"crossref","unstructured":"Shi, B., Weninger, T.: Open-world knowledge graph completion. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 1957\u20131964 (2018)","DOI":"10.1609\/aaai.v32i1.11535"},{"key":"1314_CR7","doi-asserted-by":"crossref","unstructured":"Fu, C., Chen, T., Qu, M., Jin, W., Ren, X.: Collaborative policy learning for open knowledge graph reasoning. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) EMNLP-IJCNLP 2019, pp. 2672\u20132681 (2019)","DOI":"10.18653\/v1\/D19-1269"},{"key":"1314_CR8","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: North American Chapter of the Association for Computational Linguistics (2019)"},{"key":"1314_CR9","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T.J., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., al.: Language models are few-shot learners. arXiv:2005.14165 (2020)"},{"key":"1314_CR10","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi\u00e8re, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G.: Llama: open and efficient foundation language models. ArXiv. (2023)"},{"key":"1314_CR11","doi-asserted-by":"crossref","unstructured":"Sun, T., Shao, Y., Qiu, X., Guo, Q., Hu, Y., Huang, X., Zhang, Z.: Colake: contextualized language and knowledge embedding. arXiv:2010.00309 (2020)","DOI":"10.18653\/v1\/2020.coling-main.327"},{"key":"1314_CR12","unstructured":"Sun, Y., Wang, S., Feng, S., Ding, S., Pang, C., Shang, J., Liu, J., Chen, X., Zhao, Y., Lu, Y., Liu, W., Wu, Z., Gong, W., Liang, J., Shang, Z., Sun, P., Liu, W., Ouyang, X., Yu, D., Tian, H., Wu, H., Wang, H.: ERNIE 3.0: large-scale knowledge enhanced pre-training for language understanding and generation. CoRR. arXiv:2107.02137 (2021)"},{"key":"1314_CR13","unstructured":"Yao, L., Mao, C., Luo, Y.: KG-BERT: BERT for knowledge graph completion. CoRR. arXiv:1909.03193 (2019)"},{"key":"1314_CR14","doi-asserted-by":"crossref","unstructured":"Lv, X., Lin, Y., Cao, Y., Hou, L., Li, J., Liu, Z., Li, P., Zhou, J.: Do pre-trained models benefit knowledge graph completion? a reliable evaluation and a reasonable approach. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Findings of the Association for Computational Linguistics: ACL, pp. 3570\u20133581 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.282"},{"key":"1314_CR15","doi-asserted-by":"crossref","unstructured":"Jiang, P., Agarwal, S., Jin, B., Wang, X., Sun, J., Han, J.: Text augmented open knowledge graph completion via pre-trained language models. In: Rogers, A., Boyd-Graber, J.L., Okazaki, N. (eds.) Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023, pp. 11161\u201311180 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.709"},{"key":"1314_CR16","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Bonet, B., Koenig, S. (eds.) Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2181\u20132187 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"1314_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Brodley, C.E., Stone, P. (eds.) Proceedings of the Twenty-Eighth AAAI, pp. 1112\u20131119. AAAI Press, (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"1314_CR18","unstructured":"Nickel, M., Tresp, V., Kriegel, H.: A three-way model for collective learning on multi-relational data. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning, ICML, pp. 809\u2013816 (2011)"},{"key":"1314_CR19","unstructured":"Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015)"},{"key":"1314_CR20","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: Balcan, M., Weinberger, K.Q. (eds.) Proceedings of the 33nd International Conference on Machine Learning, ICML. JMLR Workshop and Conference Proceedings, vol. 48, pp. 2071\u20132080 (2016)"},{"key":"1314_CR21","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M.S., Kipf, T.N., Bloem, P., Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., Navigli, R., Vidal, M., Hitzler, P., Troncy, R., Hollink, L., Tordai, A., Alam, M. (eds.) The Semantic Web - 15th International Conference, ESWC. Lecture Notes in Computer Science, vol. 10843, pp. 593\u2013607. Springer, (2018)","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"1314_CR22","doi-asserted-by":"crossref","unstructured":"Kim, B., Hong, T., Ko, Y., Seo, J.: Multi-task learning for knowledge graph completion with pre-trained language models. In: Scott, D., Bel, N., Zong, C. (eds.) Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, pp. 1737\u20131743 (2020)","DOI":"10.18653\/v1\/2020.coling-main.153"},{"key":"1314_CR23","doi-asserted-by":"crossref","unstructured":"Petroni, F., Rockt\u00e4schel, T., Riedel, S., Lewis, P.S.H., Bakhtin, A., Wu, Y., Miller, A.H.: Language models as knowledge bases? In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) EMNLP-IJCNLP, pp. 2463\u20132473 (2019)","DOI":"10.18653\/v1\/D19-1250"},{"key":"1314_CR24","unstructured":"Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J., Wen, J.: A survey of large language models. CoRR. (2023)"},{"key":"1314_CR25","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wang, X., Chen, J., Qiao, S., Ou, Y., Yao, Y., Deng, S., Chen, H., Zhang, N.: Llms for knowledge graph construction and reasoning: recent capabilities and future opportunities. arXiv:2305.13168 (2023)","DOI":"10.1007\/s11280-024-01297-w"},{"key":"1314_CR26","unstructured":"Yao, L., Peng, J., Mao, C., Luo, Y.: Exploring large language models for knowledge graph completion. arXiv:2308.13916 (2023)"},{"key":"1314_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, Z., Zhang, W., Chen, H.: Making large language models perform better in knowledge graph completion. arXiv:2310.06671 (2023)","DOI":"10.1145\/3664647.3681327"},{"key":"1314_CR28","unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Neural Information Processing Systems (2017)"},{"key":"1314_CR29","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp. 5998\u20136008 (2017)"},{"key":"1314_CR30","doi-asserted-by":"crossref","unstructured":"Liu, Y., Sun, Z., Li, G., Hu, W.: I know what you do not know: knowledge graph embedding via co-distillation learning. In: Hasan, M.A., Xiong, L. (eds.) Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022, pp. 1329\u20131338 (2022)","DOI":"10.1145\/3511808.3557355"},{"issue":"9","key":"1314_CR31","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Int. J. Comput. Vis. 130(9), 2337\u20132348 (2022)","journal-title":"Int. J. Comput. Vis."},{"key":"1314_CR32","doi-asserted-by":"crossref","unstructured":"Akrami, F., Saeef, M.S., Zhang, Q., Hu, W., Li, C.: Realistic re-evaluation of knowledge graph completion methods: an experimental study. In: Maier, D., Pottinger, R., Doan, A., Tan, W., Alawini, A., Ngo, H.Q. (eds.) Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, Online Conference [Portland, OR, USA], June 14-19, 2020, pp. 1995\u20132010. ACM, (2020)","DOI":"10.1145\/3318464.3380599"},{"key":"1314_CR33","doi-asserted-by":"crossref","unstructured":"Balazevic, I., Allen, C., Hospedales, T.M.: Tucker: tensor factorization for knowledge graph completion. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) EMNLP-IJCNLP, pp. 5184\u20135193. Association for Computational Linguistics, (2019)","DOI":"10.18653\/v1\/D19-1522"},{"key":"1314_CR34","doi-asserted-by":"crossref","unstructured":"Liu, F., Shareghi, E., Meng, Z., Basaldella, M., Collier, N.: Self-alignment pretraining for biomedical entity representations. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-T\u00fcr, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pp. 4228\u20134238. Association for Computational Linguistics, (2021)","DOI":"10.18653\/v1\/2021.naacl-main.334"},{"key":"1314_CR35","unstructured":"Hu, J.E., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Chen, W.: Lora: low-rank adaptation of large language models. arXiv:2106.09685 (2021)"},{"key":"1314_CR36","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), pp. 1811\u20131818 (2018)","DOI":"10.1609\/aaai.v32i1.11573"},{"issue":"8","key":"1314_CR37","doi-asserted-by":"publisher","first-page":"3879","DOI":"10.1109\/TKDE.2024.3365727","volume":"36","author":"Z Li","year":"2024","unstructured":"Li, Z., Wang, C., Wang, X., Chen, Z., Li, J.: Hje: joint convolutional representation learning for knowledge hypergraph completion. IEEE Trans. Knowl. Data Eng. 36(8), 3879\u20133892 (2024). https:\/\/doi.org\/10.1109\/TKDE.2024.3365727","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1314_CR38","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., Ji, Y.: Hitter: hierarchical transformers for knowledge graph embeddings. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event \/ Punta Cana, Dominican Republic, 7-11 November, 2021, pp. 10395\u201310407. Association for Computational Linguistics, (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.812"},{"key":"1314_CR39","doi-asserted-by":"crossref","unstructured":"Saxena, A., Kochsiek, A., Gemulla, R.: Sequence-to-sequence knowledge graph completion and question answering. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pp. 2814\u20132828. Association for Computational Linguistics, (2022)","DOI":"10.18653\/v1\/2022.acl-long.201"},{"key":"1314_CR40","unstructured":"Das, R., Dhuliawala, S., Zaheer, M., Vilnis, L., Durugkar, I., Krishnamurthy, A., Smola, A., McCallum, A.: Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net, (2018)"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-024-01314-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-024-01314-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-024-01314-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T15:02:18Z","timestamp":1740236538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-024-01314-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,18]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1314"],"URL":"https:\/\/doi.org\/10.1007\/s11280-024-01314-y","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,18]]},"assertion":[{"value":"7 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"8"}}