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

• Published in: Applied artificial intelligence Vol. 33; no. 7; pp. 656 - 667
• Main Authors: Abedini, Farhad, Keyvanpour, Mohammad Reza, Menhaj, Mohammad Bagher
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 07.06.2019; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; Heuristic; Heuristic methods; Knowledge base; Mathematical analysis; Optimization; Resource Description Framework-RDF; Tensors; Training
• ISSN: 0883-9514; 1087-6545
• DOI: 10.1080/08839514.2019.1602317

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.