In this paper, we propose a semantically enabled news exploration method to aid journalists in overcoming the information overload in today's news streams. To achieve this, our approach semantically tags news articles, calculates their relatedness through their similarity based on these tags, and creates an article graph to be browsed by an end-user. Based on related work, the Jaccard metric seemed very suitable for this task. However, when we evaluated this similarity measure through crowd- sourcing on a set of 120 article pairs, the results were only acceptable in the lower levels of relatedness, with unpredictable errors elsewhere. This reveals a need for better ground-truth data, and calls for clarification of the semantics of relatedness and similarity, and their relation