Answering queries over Semantic Web data, i.e., RDF graphs, must account for both explicit data and implicit data, entailed by the explicit data and the semantic constraints holding on them. Two main query answering techniques have been devised, namely Saturation -based (S at ) which precomputes and adds to the graph all implicit information, and Reformulation -based (R ef ) which reformulates the query based on the graph constraints, so that evaluating the reformulated query directly against the explicit data (i.e., without considering the constraints) produces the query answer. While S at is well known, R ef has received less attention so far. In particular, reformulated queries often perform poorly if the query is complex. Our demonstration showcases a large set of R ef techniques, including but not limited to one we proposed recently. The audience will be able to 1: test them against different datasets, constraints and queries, as well as different well-established systems, 2: analyze and understand the performance challenges they raise, and 3: alter the scenarios to visualize the impact on performance. In particular, we show how a cost-based R ef approach allows avoiding reformulation performance pitfalls.