Why Cognitive Systems should combine Machine Learning with Semantic Technologies

Imagine you want to build an application that helps to identify wine and cheese pairings. Applications solely based on machine learning, those ones which are based on experts' knowledge only, or a combination of both? Most of the machine learning algorithms were developed to solve a well-known problem in AI, which is called the'Knowledge Acquisition Bottleneck'. It deals with the question how subject matter experts (SMEs) can be enabled to work together with data scientists on knowledge models in an efficient and sustainable way (See also: Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling). Machine learning algorithms learn from data, and by that, successful implementations are obviously strongly related to data quality and the approaches taken to encode the semantics (meaning) of data.