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

作     者：Abedini, Farhad Keyvanpour, Mohammad Reza Menhaj, Mohammad Bagher 

作者机构：Islamic Azad Univ Qazvin Branch Fac Comp & Informat Technol Engn Qazvin Iran Alzahra Univ Dept Comp Engn Vanak Iran Amirkabir Univ Technol Dept Elect Engn Ctr Excellence Control & Robot Tehran Iran 

出 版 物：《APPLIED ARTIFICIAL INTELLIGENCE》 (Appl Artif Intell)

年 卷 期：2019年第33卷第7期

页      面：656-667页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Tensors 

摘      要：Neural tensor network (NTN) has been recently introduced to complete Resource Description Framework (RDF) knowledge bases, which has been the state-of-the-art in the field so far. An RDF knowledge base includes some facts from the real world shown as RDF triples. In the previous methods, an objective function has been used for training this type of network, and the network parameters should have been set in a way to minimize the function. For this purpose, a classic nonlinear optimization method has been used. Since many replications are needed in this method to get the minimum amount of the function, in this paper, we suggest to combine meta-heuristic optimization methods to minimize the replications and increase the speed of training consequently. So, this problem will be improved using some meta-heuristic algorithms in this new approach to specify which algorithm will get the best results on NTN and its results will be compared with the results of the former methods finally.