Neural Tensor Network Training Using Meta-Heuristic Algorithms for RDF Knowledge Bases Completion

References

• Abedini, F., M. B. Menhaj, and M. R. Keyvanpour. 2017. An MLP-based representation of neural tensor networks for the RDF data models. Journal of Neural Computing and Applications, Springer, 1–10. doi: 10.1007/s00521-017-3053-1.
   Google Scholar
• Abedini, F., M. B. Menhaj, and M. R. Keyvanpour. 2017. Neuron mathematical model representation of neural tensor network for rdf knowledge base completion. Journal of Computer & Robotics 10 (1):1–10.
   Google Scholar
• Bizer, C., J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, and S. Hellmann. 2009. DBpedia-A crystallization point for the Web of Data. Web Semantics: Science, Services and Agents on the World Wide Web 7 (3):154–65. doi:10.1016/j.websem.2009.07.002.
   Web of Science ®Google Scholar
• Bordes, A., X. Glorot, J. Weston, and Y. Bengio. 2012. Joint learning of words and meaning representations for open-text semantic parsing. In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, p. 127-135.
   Google Scholar
• Bordes, A., J. Weston, R. Collobert, and Y. Bengio. 2011. Learning structured embeddings of knowledge bases. In Proceedings of the Twenty-Fifth AAAI Conference on Artifitial Intelligence. AAAI Press: San Francisco, California. p. 301-306.
   Google Scholar
• Imani, M. B., M. R. Keyvanpour, and R. Azmi. 2013. A NOVEL EMBEDDED FEATURE SELECTION METHOD: A COMPARATIVE STUDY IN THE APPLICATION OF TEXT CATEGORIZATION. Applied Artificial Intelligence 27 (5):408–27. doi:10.1080/08839514.2013.774211.
   Web of Science ®Google Scholar
• Jenatton, R., N. Le Roux, A. Bordes, and G. Obozinski. 2012. A latent factor model for highly multi-relational data. In Proceedings of the 25th International conference on Neural Information Processing System - Volume 2. Curran Associates Inc.: Lake Tahoe, Nevada. p. 3167-3175.
   Google Scholar
• Keyvanpour, M. R., M. R. Ebrahimi, and M. Javideh. 2012. DESIGNING EFFICIENT ANN CLASSIFIERS FOR MATCHING BURGLARIES FROM DWELLING HOUSES. Applied Artificial Intelligence 26 (8):787–807. doi:10.1080/08839514.2012.718227.
   Web of Science ®Google Scholar
• Kim, H. 2017. Building a K-Pop knowledge graph using an entertainment ontology. Knowledge Management Research & Practice 15 (2):305–15. doi:10.1057/s41275-017-0056-8.
   Web of Science ®Google Scholar
• Li, T., G. Shao, W. Zuo, and S. Huang 2017, February. Genetic Algorithm for Building Optimization: State-of-the-Art Survey. In Proceedings of the 9th International Conference on Machine Learning and Computing (pp. 205–10). ACM: Singapore, Singapore.
   Google Scholar
• Liu, D., W. Li, and S.-W. Vong. 2018. Tensor complementarity problems: The GUS-property and an algorithm. In Linear and Multilinear Algebra, Taylor and Francis, 66(9): p. 1726-1749.
   Google Scholar
• Liu, X. 2018. The eigenvalues and eigenvectors of nonsingular tensors, similar tensors and tensor products. In Linear and Multilinear Algebra, Taylor and Francis, 66(9): p. 1717-1725.
   Google Scholar
• Miller, G. A. 1995. WordNet: A Lexical Database for English. Communications of the ACM 38:39–41. doi:10.1145/219717.219748.
   Web of Science ®Google Scholar
• Rutenbar, R. A. 1989. Simulated annealing algorithms: An overview. IEEE Circuits and Devices Magazine 5 (1):19–26. doi:10.1109/101.17235.
   Web of Science ®Google Scholar
• Schmidt, M., “minfuc http://people.cs.ubc.ca/schmidtm/software/minfunc.html,” 2005.
   Google Scholar
• Socher, R, D. Chen, and C. D. Manning. 2013. Reasoning with neural tensor networks for knowledge base completion. In Proceedings of the 26thional Conference on Neural Information Processing Systems - Volume 1. Curran AssociateInc.: Lake Tahoe, Nevada. p. 926-934.
   Google Scholar
• Suchanek, F. M., G. Kasneci, and G. Weikum. 2007. Yago: A core of semantic knowledge. Proceedings of the 16th international conference on World Wide Web. ACM: Banff, Alberta, Canada. doi:.
   Google Scholar
• Sutskever, I., R. Salakhutdinov, and J. B. Tenenbaum. 2009. Modelling relational data using Bayesian clustered tensor factorization. In Proceedings of the 22nd International Conference on NInformation Processing System. Curran Associates Inc. : Vancouver, British Columbia, Canada. p. 1821-1828.
   Google Scholar
• Tavoli, R., and M. Keyvanpour. 2017. A Novel Word-Spotting Method for Handwritten Documents Using an Optimization-Based Classifier. Applied Artificial Intelligence 31 (4):346–75. doi:10.1080/08839514.2017.1346964.
   Web of Science ®Google Scholar
• Turian, J., L. Ratinov, and Y. Bengio. 2010. Word representations: A simple and general method for semi-supervised learning. Proceedings of ACL : Uppsala, Sweden. p. 384–394.
   Google Scholar
• Weikum, G., J. Hoffart, and F. Suchanek. 2016. Ten years of knowledge harvesting: Lessons and challenges. Data Engineering. 39(3): 41-50.
   Google Scholar
• Zhao, Y., Y.-J. Wu, E. Levina, and J. Zhu. 2017. Link prediction for partially observed networks. Journal of Computational and Graphical Statistics 26 (3):725–33. doi:10.1080/10618600.2017.1286243.
   Web of Science ®Google Scholar