{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T10:07:20Z","timestamp":1778407640969,"version":"3.51.4"},"publisher-location":"Cham","reference-count":68,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032030276","type":"print"},{"value":"9783032030283","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-03028-3_11","type":"book-chapter","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T07:59:43Z","timestamp":1758614383000},"page":"181-197","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From Symbolic to\u00a0Neural and\u00a0Back: Exploring Knowledge Graph\u2013Large Language Model Synergies"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9916-8756","authenticated-orcid":false,"given":"Bla\u017e","family":"\u0160krlj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7330-0579","authenticated-orcid":false,"given":"Boshko","family":"Koloski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4380-0863","authenticated-orcid":false,"given":"Senja","family":"Pollak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9995-7093","authenticated-orcid":false,"given":"Nada","family":"Lavra\u010d","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"11_CR1","unstructured":"Meta AI. Llama 3.1: a 405B-parameter open-source language model (2024). https:\/\/github.com\/facebookresearch\/llama. Community license"},{"key":"11_CR2","unstructured":"Mistral AI. Mixtral 8x22b: sparse mixture-of-experts model with 141B parameters (2024). https:\/\/github.com\/mistralai\/Mixtral. Apache 2.0 License"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"76008","DOI":"10.1109\/ACCESS.2022.3191666","volume":"10","author":"MM Alam","year":"2022","unstructured":"Alam, M.M., et al.: Language model guided knowledge graph embeddings. IEEE Access 10, 76008\u201376020 (2022)","journal-title":"IEEE Access"},{"key":"11_CR4","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015)"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, pp. 3615\u20133620. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-1371"},{"issue":"8","key":"11_CR6","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A.C., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR7","unstructured":"Bommasani, R., et\u00a0al.: On the opportunities and risks of foundation models. ArXiv preprint, abs\/2108.07258 (2021)"},{"key":"11_CR8","unstructured":"Brown, T.B., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M.A., Hadsell, R., Balcan, M.-F.,Lin, H.T. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6\u201312 December 2020, Virtual (2020)"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Chao, L., He, J., Wang, T., Chu, W.: PairRE: knowledge graph embeddings via paired relation vectors. InL Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 4360\u20134369. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.336"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Chen, H., Shen, X., Lv, Q., Wang, J., Ni, X., Ye, J.: SAC-KG: exploiting large language models as skilled automatic constructors for domain knowledge graph. In: Ku, L.-W., Martins, A., Srikumar, V. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Bangkok, Thailand, pp. 4345\u20134360. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.acl-long.238"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Cocchi, F., Moratelli, N., Cornia, M., Baraldi, L., Cucchiara, R.: Augmenting multimodal LLMs with self-reflective tokens for knowledge-based visual question answering (2025)","DOI":"10.1109\/CVPR52734.2025.00859"},{"key":"11_CR12","unstructured":"Cohere. Command R: a retrieval-augmented open source language model (2024). https:\/\/github.com\/cohere-ai\/command-r. Open source for research"},{"key":"11_CR13","unstructured":"Cohere. Command R+: enhanced version for large-scale tasks (2024). https:\/\/github.com\/cohere-ai\/command-r-plus. Open source, extended capabilities"},{"key":"11_CR14","unstructured":"Google DeepMind. Gemma: Lightweight LLMs in 2B and 7B sizes (2024). https:\/\/github.com\/google-research\/gemma. Open source"},{"key":"11_CR15","unstructured":"DeepSeek. DeepSeek-v3: scaling open-source LLMs via efficient retrieval (2024). https:\/\/github.com\/deepseek-ai\/DeepSeek-V3. Open source"},{"key":"11_CR16","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"issue":"3","key":"11_CR17","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s10115-017-1100-y","volume":"55","author":"D Diefenbach","year":"2018","unstructured":"Diefenbach, D., L\u00f3pez, V., Singh, K., Maret, P.: Core techniques of question answering systems over knowledge bases: a survey. Knowl. Inf. Syst. 55(3), 529\u2013569 (2018)","journal-title":"Knowl. Inf. Syst."},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Macskassy, S.A., Perlich, C., Leskovec, J., Wang, W., Ghani, R. (eds.) The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, USA, 24\u201327 August 2014, pp. 601\u2013610. ACM (2014)","DOI":"10.1145\/2623330.2623623"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Fei, H., Ren, Y., Zhang, Y., Ji, D., Liang, X.: Enriching contextualized language model from knowledge graph for biomedical information extraction. Brief. Bioinform. 22(3), bbaa110 (2021)","DOI":"10.1093\/bib\/bbaa110"},{"issue":"23","key":"11_CR20","doi-asserted-by":"publisher","first-page":"3866","DOI":"10.3390\/electronics11233866","volume":"11","author":"I Ferrari","year":"2022","unstructured":"Ferrari, I., Frisoni, G., Italiani, P., Moro, G., Sartori, C.: Comprehensive analysis of knowledge graph embedding techniques benchmarked on link prediction. Electronics 11(23), 3866 (2022)","journal-title":"Electronics"},{"issue":"8","key":"11_CR21","doi-asserted-by":"publisher","first-page":"3549","DOI":"10.1109\/TKDE.2020.3028705","volume":"34","author":"Q Guo","year":"2022","unstructured":"Guo, Q., et al.: A survey on knowledge graph-based recommender systems. IEEE Trans. Knowl. Data Eng. 34(8), 3549\u20133568 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"11_CR22","unstructured":"Hogan, A., et al.: Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge. Morgan & Claypool Publishers (2021)"},{"issue":"4","key":"11_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447772","volume":"54","author":"A Hogan","year":"2021","unstructured":"Hogan, A., Blomqvist, E., Cochez, M., d\u2019Amato, C., De Melo, G., Gutierrez, C., Kirrane, S., Gayo, J.E.L., Navigli, R., Neumaier, S., et al.: Knowledge graphs. ACM Comput. Surv. (Csur) 54(4), 1\u201337 (2021)","journal-title":"ACM Comput. Surv. (Csur)"},{"key":"11_CR24","first-page":"1413","volume":"36","author":"H Linmei","year":"2023","unstructured":"Linmei, H., Liu, Z., Zhao, Z., Hou, L., Nie, L., Li, J.: A survey of knowledge-enhanced pre-trained language models. IEEE Trans. Knowl. Data Eng. 36, 1413\u20131430 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"11_CR25","unstructured":"Huang, H., Chen, C., He, C., Li, Y., Jiang, J., Zhang, W.: Can LLMs be good graph judger for knowledge graph construction? (2025)"},{"key":"11_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-030-45439-5_2","volume-title":"Advances in Information Retrieval","author":"N Jia","year":"2020","unstructured":"Jia, N., Cheng, X., Su, S.: Improving knowledge graph embedding using locally and globally attentive relation paths. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12035, pp. 17\u201332. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45439-5_2"},{"key":"11_CR27","unstructured":"Kim, M.J., Grinsztajn, L., Varoquaux, G.: CARTE: pretraining and transfer for tabular learning. In: Proceedings of the 41st International Conference on Machine Learning, ICML 2024. JMLR.org (2024)"},{"key":"11_CR28","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.neucom.2022.01.096","volume":"496","author":"B Koloski","year":"2022","unstructured":"Koloski, B., Perdih, T.S., Robnik-\u0160ikonja, M., Pollak, S., \u0160krlj, B.: Knowledge graph informed fake news classification via heterogeneous representation ensembles. Neurocomputing 496, 208\u2013226 (2022)","journal-title":"Neurocomputing"},{"key":"11_CR29","unstructured":"AI21 Labs. Jamba: A production-grade hybrid LLM (2024). https:\/\/github.com\/ai21labs\/Jamba. Open source under Apache 2.0"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Li, K., Zhang, T., Wu, X., Luo, H., Glass, J., Meng, H.: Decoding on graphs: faithful and sound reasoning on knowledge graphs through generation of well-formed chains. arXiv preprint arXiv:2410.18415 (2024)","DOI":"10.18653\/v1\/2025.acl-long.1186"},{"issue":"16","key":"11_CR31","doi-asserted-by":"publisher","first-page":"3171","DOI":"10.3390\/electronics13163171","volume":"13","author":"Y Li","year":"2024","unstructured":"Li, Y., Zhu, C.: TransE-MTP: a new representation learning method for knowledge graph embedding with multi-translation principles and TransE. Electronics 13(16), 3171 (2024)","journal-title":"Electronics"},{"key":"11_CR32","unstructured":"Liu, G., Zhang, Y., Li, Y., Yao, Q.: Dual reasoning: a GNN-LLM collaborative framework for knowledge graph question answering. In: Conference on Parsimony and Learning (CPAL) (2025)"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: K-BERT: enabling language representation with knowledge graph. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 7\u201312 February 2020, pp. 2901\u20132908. AAAI Press (2020)","DOI":"10.1609\/aaai.v34i03.5681"},{"key":"11_CR34","unstructured":"Ma, S., Xu, C., Jiang, X., Li, M., Qu, H., Guo, J.: Think-on-graph 2.0: deep and interpretable large language model reasoning with knowledge graph-guided retrieval. arXiv preprint arXiv:2407.10805 (2023)"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Markowitz, E., et al.: Tree-of-traversals: a zero-shot reasoning algorithm for augmenting black-box language models with knowledge graphs. arXiv preprint arXiv:2407.21358 (2024)","DOI":"10.18653\/v1\/2024.acl-long.665"},{"key":"11_CR36","unstructured":"Microsoft. Phi-3 medium: A 14B-parameter language model (2024). https:\/\/github.com\/microsoft\/phi. Open source under MIT License"},{"key":"11_CR37","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting Held December 5-8, 2013, Lake Tahoe, Nevada, United States, pp. 3111\u20133119 (2013)"},{"key":"11_CR38","unstructured":"Databricks\u00a0Mosaic ML. DBRX: an open-source mixture-of-experts language model (2024). https:\/\/github.com\/databricks\/DBRX. Open source"},{"key":"11_CR39","unstructured":"Nvidia. Nemotron-4: A 340B-parameter open source language model (2024). https:\/\/github.com\/nvidia\/Nemotron-4. Apache 2.0 License"},{"key":"11_CR40","unstructured":"Pan, J.Z., et al.: Large language models and knowledge graphs: opportunities and challenges. Trans. Graph Data Knowl. 1(1), 2:1\u20132:38 (2023)"},{"key":"11_CR41","unstructured":"Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., Wu, X.: Unifying large language models and knowledge graphs: a roadmap. arXiv preprint arXiv:2306.08302 (2023)"},{"issue":"7","key":"11_CR42","doi-asserted-by":"publisher","first-page":"3580","DOI":"10.1109\/TKDE.2024.3352100","volume":"36","author":"S Pan","year":"2024","unstructured":"Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., Xindong, W.: Unifying large language models and knowledge graphs: a roadmap. IEEE Trans. Knowl. Data Eng. 36(7), 3580\u20133599 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"11","key":"11_CR43","doi-asserted-by":"publisher","first-page":"13071","DOI":"10.1007\/s10462-023-10465-9","volume":"56","author":"C Peng","year":"2023","unstructured":"Peng, C., Xia, F., Naseriparsa, M., Osborne, F.: Knowledge graphs: opportunities and challenges. Artif. Intell. Rev. 56(11), 13071\u201313102 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"11_CR44","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532\u20131543. Association for Computational Linguistics (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"11_CR45","doi-asserted-by":"crossref","unstructured":"Peters, M.E., et al.: KnowBERT: knowledge-enhanced contextualized word representations. In: Proceedings of EMNLP 2019 (2019)","DOI":"10.18653\/v1\/D19-1005"},{"key":"11_CR46","doi-asserted-by":"crossref","unstructured":"Petroni, F., et al.: Language models as knowledge bases? In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, pp. 2463\u20132473. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-1250"},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Pound, J., Mika, P., Zaragoza, H.: Ad-hoc object retrieval in the web of data. In: Rappa, M., Jones, P., Freire, J., Chakrabarti, S. (eds.) Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, 26\u201330 April 2010, pp. 771\u2013780. ACM (2010)","DOI":"10.1145\/1772690.1772769"},{"key":"11_CR48","unstructured":"Radford, A., Jeffrey, W., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. Technical report, OpenAI Technical Report (2019)"},{"key":"11_CR49","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140:1\u2013140:67 (2020)"},{"key":"11_CR50","unstructured":"Ruffinelli, D., Broscheit, S., Gemulla, R.: You CAN teach an old dog new tricks! On training knowledge graph embeddings. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26\u201330 April 2020. OpenReview.net (2020)"},{"key":"11_CR51","doi-asserted-by":"crossref","unstructured":"Sequeda, J., Lassila, O.: Designing and Building Enterprise Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge. Morgan & Claypool Publishers (2021)","DOI":"10.1007\/978-3-031-01916-6"},{"issue":"3","key":"11_CR52","first-page":"527","volume":"13","author":"\u00d6 Sevgili","year":"2022","unstructured":"Sevgili, \u00d6., Shelmanov, A., Arkhipov, M., Panchenko, A., Biemann, C.: Neural entity linking: a survey of models based on deep learning. Semant. Web 13(3), 527\u2013570 (2022)","journal-title":"Semant. Web"},{"key":"11_CR53","doi-asserted-by":"crossref","unstructured":"Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: an open multilingual graph of general knowledge. In: Singh, S.P., Markovitch, S. (eds.) Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 4\u20139 February 2017, San Francisco, California, USA, pp. 4444\u20134451. AAAI Press (2017)","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"11_CR54","unstructured":"Sun, Y., Wang, S., Li, Y., et\u00a0al.: Ernie: enhanced representation through knowledge integration. In: AAAI (2019)"},{"key":"11_CR55","unstructured":"Thoppilan, R., et\u00a0al. LaMDA: language models for dialog applications. ArXiv preprint, abs\/2201.08239 (2022)"},{"issue":"5","key":"11_CR56","doi-asserted-by":"publisher","first-page":"104145","DOI":"10.1016\/j.ipm.2025.104145","volume":"62","author":"S Tsaneva","year":"2025","unstructured":"Tsaneva, S., Dess\u00ec, D., Osborne, F., Sabou, M.: Knowledge graph validation by integrating LLMs and human-in-the-loop. Inf. Process. Manage. 62(5), 104145 (2025)","journal-title":"Inf. Process. Manage."},{"key":"11_CR57","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4\u20139 December 2017, Long Beach, CA, USA, pp. 5998\u20136008 (2017)"},{"key":"11_CR58","unstructured":"Wang, J., et al.: Learning to plan for retrieval-augmented large language models from knowledge graphs. arXiv preprint arXiv:2406.14282 (2024)"},{"issue":"3","key":"11_CR59","doi-asserted-by":"publisher","first-page":"485","DOI":"10.3390\/sym13030485","volume":"13","author":"M Wang","year":"2021","unstructured":"Wang, M., Qiu, L., Wang, X.: A survey on knowledge graph embeddings for link prediction. Symmetry 13(3), 485 (2021)","journal-title":"Symmetry"},{"key":"11_CR60","unstructured":"Wen, H., Chen, M., Chang, K.-W., Roth, D.: MindMap: prompting large language models with knowledge graphs for graph-of-thought reasoning. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics (2024)"},{"key":"11_CR61","unstructured":"[First Name] Yang and Others. Fact-aware generation in large language models (2024). https:\/\/example.org\/yang2024factaware. Extended version available online"},{"key":"11_CR62","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.sbi.2021.09.003","volume":"72","author":"X Zeng","year":"2022","unstructured":"Zeng, X., Xinqi, T., Liu, Y., Xiangzheng, F., Yansen, S.: Toward better drug discovery with knowledge graph. Curr. Opin. Struct. Biol. 72, 114\u2013126 (2022)","journal-title":"Curr. Opin. Struct. Biol."},{"key":"11_CR63","unstructured":"Zhang, H., Guo, Z., et al.: Kg-enhanced reasoning in large language models. In: Proceedings of ACL 2024 (2024)"},{"key":"11_CR64","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, Z., Guo, L., Xu, Y., Zhang, W., Chen, H.: Making large language models perform better in knowledge graph completion. arXiv preprint arXiv:2310.06671 (2023)","DOI":"10.1145\/3664647.3681327"},{"key":"11_CR65","unstructured":"Zhang, Y., Wang, X., Liang, J., Xia, S., Chen, L., Xiao, Y.: Chain-of-knowledge: integrating knowledge reasoning into large language models by learning from knowledge graphs. arXiv preprint arXiv:2407.00653 (2024)"},{"key":"11_CR66","unstructured":"Zhao, W.X., et\u00a0al.: A survey of large language models. ArXiv preprint, abs\/2303.18223 (2023)"},{"issue":"1","key":"11_CR67","first-page":"225","volume":"139","author":"Z Zhao","year":"2024","unstructured":"Zhao, Z., Luo, X., Chen, M., Ma, L.: A survey of knowledge graph construction using machine learning. Comput. Model. Eng. Sci. 139(1), 225\u2013257 (2024)","journal-title":"Comput. Model. Eng. Sci."},{"key":"11_CR68","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: LLMs for knowledge graph construction and reasoning: recent capabilities and future opportunities. arXiv preprint arXiv:2305.13168 (2023)","DOI":"10.1007\/s11280-024-01297-w"}],"container-title":["Lecture Notes in Computer Science","Challenges and Algorithms for Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-03028-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T08:00:12Z","timestamp":1758614412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-03028-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"ISBN":["9783032030276","9783032030283"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-03028-3_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"23 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}