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杨建喜 等: 多语义视图驱动的        OWL  知识图谱表示学习方法                                          5671


                     Singapore: ACM, 2024. 2250–2258. [doi: 10.1145/3589334.3645648]
                 [24]   Zhapa-Camacho F, Hoehndorf R. Lattice-preserving ALC ontology embeddings. In: Proc. of the 18th Int’l Conf. on Neural-symbolic
                     Learning and Reasoning, Part I. Barcelona: Springer, 2024. 355–369. [doi: 10.1007/978-3-031-71167-1_19]
                 [25]   Chen JY, He Y, Geng YX, Jiménez-Ruiz E, Dong H, Horrocks I. Contextual semantic embeddings for ontology subsumption prediction.
                     World Wide Web, 2023, 26(5): 2569–2591. [doi: 10.1007/s11280-023-01169-9]
                 [26]   Chen  JY,  Hu  P,  Jimenez-Ruiz  E,  Holter  OM,  Antonyrajah  D,  Horrocks  I.  OWL2Vec*:  Embedding  of  OWL  ontologies.  Machine
                     Learning, 2021, 110(7): 1813–1845. [doi: 10.1007/s10994-021-05997-6]
                 [27]   Wang Z, Zhang JW, Feng JL, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proc. of the 28th AAAI Conf. on
                     Artificial Intelligence. Québec City: AAAI Press, 2014. 1112–1119. [doi: 10.5555/2893873.2894046]
                 [28]   Lin YK, Liu ZY, Sun MS, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In: Proc. of the 29th
                     AAAI Conf. on Artificial Intelligence. Austin: AAAI Press, 2015. 2181–2187. [doi: 10.5555/2886521.2886624]
                 [29]   Ji GL, He SZ, Xu LH, Liu K, Zhao J. Knowledge graph embedding via dynamic mapping matrix. In: Proc. of the 53rd Annual Meeting of
                     the  Association  for  Computational  Linguistics  and  the  7th  Int’l  Joint  Conf.  on  Natural  Language  Processing  (Vol.  1:  Long  Papers).
                     Beijing: Association for Computational Linguistics, 2015. 687–696. [doi: 10.3115/v1/P15-1067]
                 [30]   Nickel M, Tresp V, Kriegel HP. A three-way model for collective learning on multi-relational data. In: Proc. of the 28th Int’l Conf. on Int’l
                     Conf. on Machine Learning. Bellevue: Omnipress, 2011. 809–816. [doi: 10.5555/3104482.3104584]
                 [31]   Yang BS, Yih WT, He XD, Gao JF, Deng L. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In: Proc.
                     of the 3rd Int’l Conf. on Learning Representations. San Diego, 2015.
                 [32]   Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G. Complex embeddings for simple link prediction. In: Proc. of the 33rd Int’l Conf.
                     on Int’l Conf. on Machine Learning. New York: JMLR, 2016. 2071–2080. [doi: 10.5555/3045390.3045609]
                 [33]   Tay Y, Luu A, Hui SC. Non-parametric estimation of multiple embedding for link prediction on dynamic knowledge graphs. In: Proc. of
                     the 2017 AAAI Conf. on Artificial Intelligence. San Francisco: AAAI Press, 2017. 1243–1249. [doi: 10.1609/aaai.v31i1.10685]
                 [34]   Jia  YT,  Wang  YZ,  Jin  XL,  Lin  HL,  Cheng  XQ.  Knowledge  graph  embedding:  A  locally  and  temporally  adaptive  translation-based
                     approach. ACM Trans. on the Web, 2017, 12(2): 8. [doi: 10.1145/3132733]
                 [35]   Daruna A, Gupta M, Sridharan M, Chernova S. Continual learning of knowledge graph embeddings. IEEE Robotics and Automation
                     Letters, 2021, 6(2): 1128–1135. [doi: 10.1109/LRA.2021.3056071]
                 [36]   Wu TX, Khan A, Yong M, Qi GL, Wang M. Efficiently embedding dynamic knowledge graphs. Knowledge-based Systems, 2022, 250:
                     109124. [doi: 10.1016/j.knosys.2022.109124]
                 [37]   Yao SY, Zhao TZ, Wang RJ, Liu J. Rule-guided joint embedding learning of knowledge graphs. Journal of Computer Research and
                     Development, 2020, 57(12): 2514–2522 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.2020.20200741]
                 [38]   Nayyeri M, Wang ZH, Akter MM, Alam MM, Rony MRAH, Lehmann J, Staab S. Integrating knowledge graph embeddings and pre-
                     trained language models in hypercomplex spaces. In: Proc. of the 22nd Int’l Semantic Web Conf. on the Semantic Web (ISWC 2023).
                     Athens: Springer, 2023. 388–407. [doi: 10.1007/978-3-031-47240-4_21]
                 [39]   Xiao  L,  Shan  X,  Wang  YH,  Deng  ML.  Research  on  joint  representation  learning  methods  for  entity  neighborhood  information  and
                     description information. In: Proc. of the 8th China Conf. on Knowledge Graph and Semantic Computing: Knowledge Graph Empowers
                     Artificial General Intelligence. Shenyang: Springer, 2023. 41–53. [doi: 10.1007/978-981-99-7224-1_4]
                 [40]   Ning YL, Zhou G, Lu JC, Yang DW, Zhang T. A representation learning method of knowledge graph integrating relation path and entity
                     description information. Journal of Computer Research and Development, 2022, 59(9): 1966–1979 (in Chinese with English abstract). [10.
                     7544/issn1000-1239.20210651]
                 [41]   Hu  XY,  Wang  ZZ,  Sun  YY,  Xu  B,  Lin  HF.  Knowledge  graph  representation  method  combined  with  semantic  parsing.  Journal  of
                     Computer  Research  and  Development,  2022,  59(12):  2878–2888  (in  Chinese  with  English  abstract).  [doi:  10.7544/issn1000-1239.
                     20210849]
                 [42]   Shu D, Chen TL, Jin MY, Zhang C, Du MN, Zhang YF. Knowledge graph large language model (KG-LLM) for link prediction. In: Proc.
                     of the 16th Asian Conf. on Machine Learning. Hanoi: PMLR, 2025. 143–158.
                 [43]   Yao L, Peng JZ, Mao CS, Luo Y. Exploring large language models for knowledge graph completion. In: Proc. of the 2025 IEEE Int’l
                     Conf. on Acoustics, Speech and Signal Processing (ICASSP 2025). Hyderabad: IEEE, 2025. 1–5. [doi: 10.1109/ICASSP49660.2025.
                     10889242]
                 [44]   Hitzler P, Krötzsch M, Parsia B, Patel-Schneider PF, Rudolph S. OWL 2 Web ontology language primer. W3C Recommendation, 2009,
                     27(1): 123.
                 [45]   Rodríguez-García MÁ, Hoehndorf R. Inferring ontology graph structures using OWL reasoning. BMC bioinformatics, 2018, 19(1): 7.
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