With so much opinionated, but unstructured, data available on the Web, sentiment analysis has become popular with both companies and researchers. Aspect-based sentiment analysis goes one step further by relating the expressed sentiment in a text to the topic, or aspect, the sentiment is expressed on. This enables a detailed analysis of the sentiment expressed in, for example, reviews of products or services. In this paper we propose a knowledge-driven approach to aspect sentiment analysis that complements traditional machine learning methods. By utilizing common domain knowledge, as encoded in an ontology, we improve the sentiment analysis of a given aspect. The domain knowledge is used to determine which words are expressing sentiment on the given aspect as well as to disambiguate sentiment carrying words or phrases. The proposed method has a highly competitive performance of over 80% accuracy on both SemEval-2015 and SemEval-2016 data, significantly outperforming the considered baselines.