Page 289 - 《软件学报》2025年第12期
P. 289

5670                                                      软件学报  2025  年第  36  卷第  12  期


                 噪声数据的问题, 作者团队也将在未来的工作中对其展开研究, 提升模型方法的鲁棒性以及多种问题的处理能力.

                 References:
                  [1]   Wang M, Wang HF, Li BH, Zhao X, Wang X. Survey on key technologies of new generation knowledge graph. Journal of Computer
                     Research and Development, 2022, 59(9): 1947–1965 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.20210829]
                  [2]   Peng  CY,  Xia  F,  Naseriparsa  M,  Osborne  F.  Knowledge  graphs:  Opportunities  and  challenges.  Artificial  Intelligence  Review,  2023,
                     56(11): 13071–13102. [doi: 10.1007/s10462-023-10465-9]
                  [3]   Chen Y, Wu LF, Zaki MJ. Toward subgraph-guided knowledge graph question generation with graph neural networks. IEEE Trans. on
                     Neural Networks and Learning Systems, 2024, 35(9): 12706–12717. [doi: 10.1109/TNNLS.2023.3264519]
                  [4]   Yang JX, Yang XX, Li R, Luo MT, Jiang SX, Zhang Y, Wang D. BERT and hierarchical cross attention-based question answering over
                     bridge inspection knowledge graph. Expert Systems with Applications, 2023, 233: 120896. [doi: 10.1016/j.eswa.2023.120896]
                  [5]   Zhao N, Long Z, Wang J, Zhao ZD. AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN
                     encoder. Knowledge-based Systems, 2023, 259: 110078. [doi: 10.1016/j.knosys.2022.110078]
                  [6]   Xia LQ, Liang YS, Leng JW, Zheng P. Maintenance planning recommendation of complex industrial equipment based on knowledge
                     graph and graph neural network. Reliability Engineering & System Safety, 2023, 232: 109068. [doi: 10.1016/j.ress.2022.109068]
                  [7]   World-Wide Web Consortium. RDF 1.1 Primer. Boston: World-Wide Web Consortium, 2014.
                  [8]   Breitman KK, Casanova MA, Truszkowski W. RDF and RDF schema. Semantic Web: Concepts, Technologies and Applications, 2007,
                     57–79.
                  [9]   Hogan A. RDF schema and semantics. In: Hogan A. The Web of Data. Cham: Springer, 2020: 111–183. [doi: 10.1007/978-3-030-51580-
                     5_4]
                 [10]   McGuinness DL, van Harmelen F. OWL Web ontology language—Overview. 2004. https://www.w3.org/TR/owl-features/
                 [11]   Antoniou G, van Harmelen F. Web ontology language: OWL. In: Staab S, Studer R. Handbook on Ontologies. Heidelberg: Springer,
                     2009: 91–110. [doi: 10.1007/978-3-540-92673-3_4]
                 [12]   Bollacker  K,  Evans  C,  Paritosh  P,  Sturge  T,  Taylor  J.  Freebase:  A  collaboratively  created  graph  database  for  structuring  human
                     knowledge. In: Proc. of the 2008 ACM SIGMOD Int’l Conf. on Management of Data. Vancouver: ACM, 2008. 1247–1250. [doi: 10.1145/
                     1376616.1376746]
                 [13]   Lehmann  J,  Isele  R,  Jakob  M,  Jentzsch  A,  Kontokostas  D,  Mendes  PN,  Hellmann  S,  Morsey  M,  van  Kleef  P,  Auer  S,  Bizer  C.
                     DBpedia—A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web, 2015, 6(2): 167–195. [doi: 10.3233/SW-
                     140134]
                 [14]   Dragoni M, Bailoni T, Maimone R, Eccher C. HeLiS: An ontology for supporting healthy lifestyles. In: Proc. of the 17th Int’l Semantic
                     Web Conf. on the Semantic Web. Monterey: Springer, 2018. 53–69. [doi: 10.1007/978-3-030-00668-6_4]
                 [15]   Dooley DM, Griffiths EJ, Gosal GS, Buttigieg PL, Hoehndorf R, Lange MC, Schriml LM, Brinkman FSL, Hsiao WWL. FoodOn: A
                     harmonized food ontology to increase global food traceability, quality control and data integration. npj Science of Food, 2018, 2(1): 23.
                     [doi: 10.1038/s41538-018-0032-6]
                 [16]   The Gene Ontology Consortium. The gene ontology project in 2008. Nucleic Acids Research, 2008, 36(S1): D440–D444. [doi: 10.1093/
                     nar/gkm883]
                 [17]   Zhong  LF,  Wu  J,  Li  Q,  Peng  H,  Wu  XD.  A  comprehensive  survey  on  automatic  knowledge  graph  construction.  ACM  Computing
                     Surveys, 2024, 56(4): 94. [doi: 10.1145/3618295]
                 [18]   Du XY, Liu MW, Shen LW, Peng X. Survey on representation learning methods of knowledge graph for link prediction. Ruan Jian Xue
                     Bao/Journal of Software, 2024, 35(1): 87–117 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6902.htm [doi: 10.
                     13328/j.cnki.jos.006902]
                 [19]   Bordes A, Usunier N, Garcia-Durán A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Proc. of
                     the 27th Int’l Conf. on Neural Int’l Processing Systems—Vol. 2. Lake Tahoe: Curran Associates Inc., 2013. 2787–2795. [doi: 10.5555/
                     2999792.2999923]
                 [20]   Xiao H, Huang ML, Hao Y, Zhu XY. TransG: A generative mixture model for knowledge graph embedding. arXiv:1509.05488, 2017.
                 [21]   Zhu  YL,  Yang  XP,  Wang  L,  Zhang  ZY.  TransRD:  Embedding  of  knowledge  graph  with  asymmetric  features.  Journal  of  Chinese
                     Information Processing, 2019, 33(11): 73–82 (in Chinese with English abstract). [doi: 10.3969/j.issn.1003-0077.2019.11.009]
                 [22]   Li  Z,  Liu  X,  Wang  X,  Liu  PK,  Shen  YX.  TransO:  A  knowledge-driven  representation  learning  method  with  ontology  information
                     constraints. World Wide Web, 2023, 26(1): 297–319. [doi: 10.1007/s11280-022-01016-3]
                                                                             ++
                 [23]   Jackermeier  M,  Chen  JY,  Horrocks  I.  Dual  box  embeddings  for  the  description  logic  EL .  In:  Proc.  of  the  2024  ACM  Web  Conf.
   284   285   286   287   288   289   290   291   292   293   294