'WhoKnows?' is a simple Q&A Game in the style of 'Who wants to be a Millionaire'. You will answer questions that are automatically generated from DBpedia content.

The purpose is the evaluation of some heuristics that are used to determine a ranking of facts within a knowledge base such as e.g. DBpedia.

These are the simple assumptions 'WhoKnows?' is based on:

1. If a user knows the correct answer, the fact seems to be 'important'.
2. If a user doesn't know the correct answer, the fact seems to be not so 'important'.
3. If a user votes the question to be wrong, odd, or strange, the fact seems to be 'irrelevant'.

There are different variants to play the game:

1. One-on-One questions: only one choice is correct.
2. N-to-One questions: there are multiple correct answers.
3. Hangman: find the answer by playing the popular game of hangman.
4. Maths: find the answer and compute a simple arithmetic formula.

Meanwhile you will receive points for correct answers. The faster you provide the answer, the more points you will get. If you provide the wrong answer, you'll loose a life and some points will be taken from your score.

RISQ! (Renowned Individuals Semantic Quiz) is a jeopardy-style game with questions generated from DBpedia content. You will answer questions about famous people that are selected from wide range of domains, as e.g., celebrities, politicians, scientists, artists or sportsmen. A set of possible answers is suggested of which you should choose wisely. You can earn money with the right answers or risq it with the wild guesses.

Hence questions are rather challenging you will be tempted to buy hints (clues) that provide more background knowledge on the prospected person. These clues are generated by harnessing Property-Object-Values of a subject in question, e.g. (x isPresidentOf Germany). We evaluate, which clues lead to the right answers to deduce relevant facts from a knowledge base. In the end RISQ! will improve future semantic web applications because it helps to choose the relevant properties that can be considered for navigational features or filter facets and smart recommendations.