Over the last years, enormous amounts of opinions have become available on the Web, which causes the interest in the task of Aspect-Based Sentiment Classification (ABSC) to rise. Hybrid models for ABSC, which combine a knowledge base with a machine learning algorithm, have gained popularity because of their superior performance. LCR-Rot-hop-ont++ is such a hybrid model, which injects knowledge from a domain sentiment ontology into the well-performing attention neural network LCR-Rot-hop++. In this work, we extend the LCR-Rot-hop-ont++ model in two ways. First, we inject additional knowledge from a domain sentiment ontology into the neural network. Second, we apply a novel weighting mechanism to the injected tokens to control the influence of additional knowledge. Using the SemEval 2015 and SemEval 2016 datasets for evaluation, we find that knowledge injection improves accuracy for datasets with a limited number of observations. Furthermore, we find that the proposed weighting mechanism leads to improved predictive performance of the neural network model.