<p>Unification is indispensable for inferences in symbolic processing. The authors propose a neural network-based solution to perform efficient unification. Symbolic processing in conventional artificial intelligence has strong inference abilities but is not well suited for handling large amounts of ambiguous data. Contrastingly, neural networks can easily handle large amounts of ambiguous data but are not well suited for making complex inferences. Therefore, the authors realized the unification of the knowledge base, including ambiguous data, using a network that combines a memory network and recurrent neural network. The novelty of the proposed network is that matching, which is a unification process, is highly efficient and substitution, which is a unification process, is robust. The proposed network enables highly efficient matching by grouping multiple terms and processing them in a memory network. Furthermore, it can handle unknown words even during substitution because it uses a recurrent neural network to perform substitution. The experimental results show that the proposed network can achieve more efficient unification of ambiguous data than the baseline. This study combines symbolic processing and deep learning and suggests that it contributes to the realization of complex inferences from large amounts of ambiguous data, which has proven challenging in conventional research. Furthermore, the use of unification, which handles large amounts of ambiguous data, facilitates the development of inference systems with human-interactive interfaces. This allows humans to obtain inference results without knowing the representations of the knowledge base.</p>