{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T16:30:08Z","timestamp":1769185808893,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819555666","type":"print"},{"value":"9789819555673","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-5567-3_10","type":"book-chapter","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:14:11Z","timestamp":1769116451000},"page":"136-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HyperKGC: Hypergraph-Enhanced Multimodal Knowledge Graph Completion with\u00a0Dynamic Fusion"],"prefix":"10.1007","author":[{"given":"Huadong","family":"Chen","sequence":"first","affiliation":[]},{"given":"Xiaoyan","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Bang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Taisong","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Bala\u017eevi\u0107, I., Allen, C., Hospedales, T.M.: Tucker: tensor factorization for knowledge graph completion. arXiv preprint arXiv:1901.09590 (2019)","DOI":"10.18653\/v1\/D19-1522"},{"key":"10_CR2","unstructured":"Bao, H., Dong, L., Piao, S., Wei, F.: Beit: bert pre-training of image transformers. arXiv preprint arXiv:2106.08254 (2021)"},{"key":"10_CR3","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. NeurIPS, 26 (2013)"},{"key":"10_CR4","unstructured":"Cao, Z., Xu, Q., Yang, Z., He, Y., Cao, X., Huang, Q.: Otkge: multi-modal knowledge graph embeddings via optimal transport. NeurIPS 35 (2022)"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Hybrid transformer with multi-level fusion for multimodal knowledge graph completion. In: SIGIR, pp. 904\u2013915 (2022)","DOI":"10.1145\/3477495.3531992"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.:Convolutional 2d knowledge graph embeddings. In: AAAI, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M. W., Lee, K., Toutanova, K.:Bert: pre-training of deep bidirectional transformers for language understanding. In :NAACL, pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"3558","DOI":"10.1609\/aaai.v33i01.33013558","volume":"33","author":"Y Feng","year":"2019","unstructured":"Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. AAAI 33, 3558\u20133565 (2019)","journal-title":"AAAI"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: SIGKDD, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Hogan, A., Blomqvist, E., Cochez, M., d\u2019Amato, C., Melo, G. D., Gutierrez, C., Zimmermann, A.: Knowledge graphs. In: ACM Computing, vol. 54 (2021)","DOI":"10.1145\/3447772"},{"key":"10_CR11","unstructured":"Kipf, T. N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Lee, J., Chung, C., Lee, H., Jo, S., Whang, J.: VISTA: visual-textual knowledge graph representation learning. In: EMNLP, pp. 7314\u20137328 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.488"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Liang, K., et al.: A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE Trans. Pattern Anal. Mach. Intell. 46(12), 9456\u20139478 (2024)","DOI":"10.1109\/TPAMI.2024.3417451"},{"key":"10_CR14","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: AAAI, vol. 29 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: Chemvlm: exploring the power of multimodal large language models in chemistry area. In: AAAI, vol. 39 (2025)","DOI":"10.1609\/aaai.v39i1.32020"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Li, X., Zhao, X., Xu, J., Zhang, Y., Xing, C.: IMF: interactive multimodal fusion model for link prediction. In: WWW, pp. 2572\u20132580 (2023)","DOI":"10.1145\/3543507.3583554"},{"key":"10_CR17","unstructured":"Nickel, M., Tresp, V., Kriegel, H. P.: A three-way model for collective learning on multi-relational data. In: ICML, pp. 809\u2013816 (2011)"},{"key":"10_CR18","unstructured":"Sun, Z., Deng, Z. H., Nie, J. Y., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)"},{"key":"10_CR19","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, pp. 2071\u20132080 (2016)"},{"key":"10_CR20","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS, vol. 30 (2017)"},{"key":"10_CR21","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: ICLR (2017)"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Wang, M., Wang, S., Yang, H., Zhang, Z., Chen, X., Qi, G.: Is visual context really helpful for knowledge graph? A representation learning perspective. In: ACM MM, pp. 2735\u20132743 (2021)","DOI":"10.1145\/3474085.3475470"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Wang, X.: Developing multimodal healthcare foundation model: from data-driven to knowledge-enhanced. In: AAAI, pp. 29305\u201329306 (2025)","DOI":"10.1609\/aaai.v39i28.35230"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Wang, X., Meng, B., Chen, H., Meng, Y., Lv, K., Zhu, W.: TIVA-KG: a multimodal knowledge graph with text, image, video and audio. In: ACM MM, pp. 2391\u20132399 (2023)","DOI":"10.1145\/3581783.3612266"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, L., Li, Q., Zeng, D.: Multimodal data enhanced representation learning for knowledge graphs. In: IJCNN, pp. 1\u20138 (2019)","DOI":"10.1109\/IJCNN.2019.8852079"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.:Knowledge graph embedding by translating on hyperplanes. In: AAAI, vol. 28 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"10_CR27","unstructured":"Xie, R., Liu, Z., Sun, M.:Representation learning of knowledge graphs with hierarchical types. In: IJCAI, pp. 2965\u20132971 (2016)"},{"key":"10_CR28","unstructured":"Yang, B., Yih, W. T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: ICLR (2015)"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Ye, Y., et al.: Harnessing multimodal large language models for multimodal sequential recommendation. In: AAAI, vol. 39 (2025)","DOI":"10.1609\/aaai.v39i12.33426"},{"key":"10_CR30","unstructured":"Zhang, S., Tay, Y., Yao, L., Liu, Q.: Quaternion knowledge graph embeddings. In: NeurIPS, vol. 32 (2019)"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Tokenization, fusion, and augmentation: towards fine-grained multi-modal entity representation. In: AAAI, pp. 13322\u201313330 (2025)","DOI":"10.1609\/aaai.v39i12.33454"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Native: multi-modal knowledge graph completion in the wild. In: SIGIR, pp. 91\u2013101 (2024)","DOI":"10.1145\/3626772.3657800"},{"key":"10_CR33","unstructured":"Zhang, Y., Zhang, W.: Knowledge graph completion with pre-trained multimodal transformer and twins negative sampling. arXiv preprint arXiv:2209.07084 (2022)"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: Mose: modality split and ensemble for multimodal knowledge graph completion. In: EMNLP, pp. 10527\u201310536 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.719"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Zheng, S., Wang, W., Qu, J., Yin, H., Chen, W., Zhao, L.: Mmkgr: multi-hop multi-modal knowledge graph reasoning. In: ICDE, pp. 96\u2013109 (2023)","DOI":"10.1109\/ICDE55515.2023.00015"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Multi-modal knowledge graph construction and application: a survey. In: ICDE, pp. 715\u2013735 (2022)","DOI":"10.1109\/TKDE.2022.3224228"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5567-3_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:14:17Z","timestamp":1769116457000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5567-3_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819555666","9789819555673"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5567-3_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"23 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}