{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T21:12:18Z","timestamp":1758057138814,"version":"3.44.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"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":["Cluster Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s10586-025-05116-3","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T11:31:27Z","timestamp":1755603087000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Relation cross-fusion attention assistance networks for multi-hop question answering over knowledge graphs"],"prefix":"10.1007","volume":"28","author":[{"given":"Yana","family":"Lv","sequence":"first","affiliation":[]},{"given":"Haomiao","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Xiuli","family":"Du","sequence":"additional","affiliation":[]},{"given":"Shaoming","family":"Qiu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"5116_CR1","doi-asserted-by":"crossref","unstructured":"Lan, Y., Wang, S., Jiang, J.: Multi-hop knowledge base question answering with an iterative sequence matching model. In: IEEE International Conference on Data Mining (ICDM). pp. 359\u2013368. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00046"},{"key":"5116_CR2","doi-asserted-by":"publisher","unstructured":"Hao, Y., Zhang, Y., Liu, K., He, S., Liu, Z., Wu, H., Zhao, J.: An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 221\u2013231. Association for Computational Linguistics, Vancouver, Canada (2017). https:\/\/doi.org\/10.18653\/v1\/P17-1021","DOI":"10.18653\/v1\/P17-1021"},{"key":"5116_CR3","doi-asserted-by":"crossref","unstructured":"Huang, X., Zhang, J., Li, D., Li, P.: Knowledge graph embedding based question answering. In: Proceedings of the Twelth ACM International Conference on Web Search and Data Mining. pp. 105\u2013113 (2019)","DOI":"10.1145\/3289600.3290956"},{"key":"5116_CR4","doi-asserted-by":"publisher","unstructured":"Petrochuk, M., Zetlemoyer, L.: Simple questions nearly solved: a new upperbound and baseline approach. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 554\u2013558. Association for Computational Linguistics, Brussels, Belgium (2018). https:\/\/doi.org\/10.18653\/v1\/D18-1051","DOI":"10.18653\/v1\/D18-1051"},{"key":"5116_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yang, J., Zhao, H.: Retrospective reader for machine reading comprehension. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 16, pp. 14506\u201314514 (2021)","DOI":"10.1609\/aaai.v35i16.17705"},{"key":"5116_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Dai, H., Kozareva, Z., Smola, A.J., Song, L.: Variational reasoning for question answering with knowledge graph. In: Thirty-Second AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)","DOI":"10.1609\/aaai.v32i1.12057"},{"key":"5116_CR7","doi-asserted-by":"crossref","unstructured":"Dua, D., Singh, S., Gardner, M.: Benefits of intermediate annotations in reading comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 5627\u20135634 (2020)","DOI":"10.18653\/v1\/2020.acl-main.497"},{"key":"5116_CR8","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neucom.2022.10.023","volume":"515","author":"W Wu","year":"2023","unstructured":"Wu, W., Zhu, Z., Qi, J., Wang, W., Zhang, G., Liu, P.: A dynamic graph expansion network for multi-hop knowledge base question answering. Neurocomputing 515, 37\u201347 (2023)","journal-title":"Neurocomputing"},{"key":"5116_CR9","doi-asserted-by":"publisher","unstructured":"Saxena, A., Tripathi, A., Talukdar, P.: Improving Multi-hop question answering over knowledge graphs using knowledge base embeddings. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 4498\u20134507. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.412","DOI":"10.18653\/v1\/2020.acl-main.412"},{"key":"5116_CR10","doi-asserted-by":"publisher","unstructured":"Sun, H., Bedrax-Weiss, T., Cohen, W.: PullNet: Open domain question answering with iterative retrieval on knowledge bases and text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 2380\u20132390. Association for Computational Linguistics, Hong Kong, China (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1242","DOI":"10.18653\/v1\/D19-1242"},{"key":"5116_CR11","doi-asserted-by":"publisher","unstructured":"Shi, J., Cao, S., Hou, L., Li, J., Zhang, H.: TransferNet: an effective and transparent framework for multi-hop question answering over relation graph. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 4149-4158. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.341","DOI":"10.18653\/v1\/2021.emnlp-main.341"},{"key":"5116_CR12","unstructured":"Das, R., Dhuliawala, S., Zaheer, M., Vilnis, L., Durugkar, I., Krishnamurthy, A., McCallum, A.: Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning. In: International Conference on Learning Representations (2018)"},{"key":"5116_CR13","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Wang, Y., Jin, X., Zhang, K.: Stepwise reasoning for multi-relation question answering over knowledge graph with weak supervision. In: Proceedings of the 13th International Conference on Web Search and Data Mining. pp. 474\u2013482 (2020)","DOI":"10.1145\/3336191.3371812"},{"issue":"7","key":"5116_CR14","doi-asserted-by":"publisher","first-page":"5513","DOI":"10.1007\/s00521-022-07965-0","volume":"35","author":"X Cao","year":"2023","unstructured":"Cao, X., Zhao, Y., Shen, B.: Improving and evaluating complex question answering over knowledge bases by constructing strongly supervised data. Neural Comput. Appl. 35(7), 5513\u20135533 (2023)","journal-title":"Neural Comput. Appl."},{"key":"5116_CR15","doi-asserted-by":"crossref","unstructured":"He, G., Lan, Y., Jiang, J., Zhao, W.X., Wen, J.-R.: Improving multi-hop knowledge base question answering by learning intermediate supervision signals. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining. pp. 553\u2013561 (2021)","DOI":"10.1145\/3437963.3441753"},{"key":"5116_CR16","first-page":"1","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 1 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5116_CR17","doi-asserted-by":"crossref","unstructured":"Chun-Fu, R., Chen, Q., Fan, R.P.: Crossvit: Cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 357-366. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"5116_CR18","doi-asserted-by":"publisher","first-page":"7401","DOI":"10.1007\/s10586-024-04373-y","volume":"27","author":"C Dong","year":"2024","unstructured":"Dong, C., Tang, Y., Zhang, L.: GHA-Inst: a real-time instance segmentation model utilizing YOLO detection framework. Clust. Comput. 27, 7401\u20137415 (2024). https:\/\/doi.org\/10.1007\/s10586-024-04373-y","journal-title":"Clust. Comput."},{"key":"5116_CR19","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classiication with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings. Preprint at http:\/\/arxiv.org\/abs\/1609.02907 (2017)"},{"issue":"6","key":"5116_CR20","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.ipm.2022.103076","volume":"59","author":"Z Xie","year":"2022","unstructured":"Xie, Z., Zhu, R., Liu, J., Zhou, G., Huang, J.X.: An eiciency relation-speciic graph transformation network for knowledge graph representation learning. Inf. Process. Manage. 59(6), 20 (2022). https:\/\/doi.org\/10.1016\/j.ipm.2022.103076","journal-title":"Inf. Process. Manage."},{"key":"5116_CR21","doi-asserted-by":"crossref","unstructured":"Pang, S., Xue, Y., Yan, Z., Huang, W., Feng, J.: Dynamic and multi-channel graph convolutional networks for aspect-based sentiment analysis. In: Findings of the Association for Computational Linguistics (ACL-IJCNLP). pp. 2627\u20132636 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.232"},{"key":"5116_CR22","doi-asserted-by":"publisher","unstructured":"Sun, H., Dhingra, B., Zaheer, M., Mazaitis, K., Salakhutdinov, R., Cohen, W.: Open domain question answering using early fusion of knowledge bases and text. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 4231\u20134242. Association for Computational Linguistics, Brussels, Belgium (2018). https:\/\/doi.org\/10.18653\/v1\/D18-1455","DOI":"10.18653\/v1\/D18-1455"},{"key":"5116_CR23","doi-asserted-by":"crossref","unstructured":"Pang, S., Xue, Y., Yan, Z., Huang, W., Feng, J.: Dynamic and multi-channel graph convolutional networks for aspect-based sentiment analysis. In: Findings of the Association for Computational Linguistics(ACL-IJCNLP). pp. 2627\u20132636 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.232"},{"key":"5116_CR24","doi-asserted-by":"crossref","unstructured":"Li, R., Chen, H., Feng, F., Ma, Z., Wang, X., Hovy, E.: Dual graph convolutional networks for aspect-based sentiment analysis. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 1, pp. 6319\u20136329 (2021)","DOI":"10.18653\/v1\/2021.acl-long.494"},{"key":"5116_CR25","doi-asserted-by":"crossref","unstructured":"Lyu, Q., Havaldar, S., Stein, A., et al.: Faithful chain-of-thought reasoning[C]. In: Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, vol. 1, pp. 305\u2013329 (2023)","DOI":"10.18653\/v1\/2023.ijcnlp-main.20"},{"issue":"10","key":"5116_CR26","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2629489","volume":"57","author":"D Vrandecic","year":"2014","unstructured":"Vrandecic, D., Kr\u00f6tzsch, M.: Wikidata: a free collaborative knowledge base. Commun. ACM 57(10), 78\u201385 (2014)","journal-title":"Commun. ACM"},{"key":"5116_CR27","doi-asserted-by":"crossref","unstructured":"Yih, W.-T., Richardson, M., Meek, C., Chang, M.-W., Suh, J.: The value of semantic parse labeling for knowledge base question answering. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 201\u2013206 (2016)","DOI":"10.18653\/v1\/P16-2033"},{"key":"5116_CR28","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. pp. 1247\u20131250 (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"5116_CR29","doi-asserted-by":"crossref","unstructured":"Talmor, A., Berant, J.: The web as a knowledge-base for answering complex questions. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 641\u2013651. Association for Computational Linguistics, New Orleans, Louisiana (2018)","DOI":"10.18653\/v1\/N18-1059"},{"key":"5116_CR30","unstructured":"Cohen, W.W., Sun, H., Hofer, R.A., Siegler, M.: Scalable neural methods for reasoning with a symbolic knowledge base. In: International Conference on Learning Representations"},{"key":"5116_CR31","first-page":"2","volume":"26","author":"A Bordes","year":"2013","unstructured":"Bordes, A., Usunier, N., Garcia, D., Alberto, W., Jason, A., Oksana, Y.: Translating embeddings for modeling multi-relational data. Adv. Neural Inf. Process. Syst. 26, 2 (2013)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5116_CR32","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning. pp. 2071\u20132080. PMLR (2016)"},{"key":"5116_CR33","first-page":"1","volume":"32","author":"DA Hudson","year":"2019","unstructured":"Hudson, D.A., Manning, C.D.: Learning by abstraction: the neural state machine. Adv. Neural Inf. Process. Syst. 32, 1 (2019)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5116_CR34","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. Preprint at http:\/\/arxiv.org\/abs\/1503.02531 (2015)"},{"key":"5116_CR35","doi-asserted-by":"crossref","unstructured":"Cao, Y., Li, X., Liu, H., Dai, W., Chen, S., Wang, B., Chen, M., Hershcovich, D.: Pay more attention to relation exploration for knowledge base question answering. In: Findings of the Association for Computational Linguistics(ACL). pp. 2119\u20132136. Association for Computational Linguistics, Toronto, Canada (2023)","DOI":"10.18653\/v1\/2023.findings-acl.133"},{"key":"5116_CR36","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"5116_CR37","unstructured":"Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Han, J.: On the variance of the adaptive learning rate and beyond. In: 8th International Conference on Learning Representations, ICLR 2020 (2020)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05116-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05116-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05116-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T19:06:38Z","timestamp":1757963198000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05116-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"references-count":37,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5116"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05116-3","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,8,19]]},"assertion":[{"value":"19 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No ethical approval was required. The study protocol adhered to the guidelines established by the journal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All authors have approved the submission of this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This study did not involve human or animal subjects.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving in human and animal participants"}}],"article-number":"491"}}