Semantic Games

Linked Data is a perfect source to generate quiz games for arbitrary purposes. Games provide an incentive for many people to test or to challenge their knowledge. While playing the games all players can contribute to various tasks including ground-truth generation, data cleansing, or simply assessment. We aim to harness games-with-a-purpose (GWAP) approaches to create and curate semantic content.


'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. .

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

  • If a user knows the correct answer, the fact seems to be 'important'.
  • If a user doesn't know the correct answer, the fact seems to be not so 'important'.
  • 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:

  • One-on-One questions: only one choice is correct.
  • N-to-One questions: there are multiple correct answers.
  • Hangman: find the answer by playing the popular game of hangman.
  • 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.


'WhoKnows?Movies!' is a derivative game of WhoKnows?. It bases on data about sixty movies originating from Freebase. The game results have been used to create a ground truth to evaluate entity summaries in the exemplary domain of movies.


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.


  1. Andreas Thalhammer, Magnus Knuth, Harald Sack: Evaluating Entity Summarization Using a Game-Based Ground Truth In Proceedings of 11th International Semantic Web Conference (ISWC 2012), Boston, MA, USA, 11 2012 Springer.
  2. Jörg Waitelonis, Nadine Ludwig, Magnus Knuth, Harald Sack: WhoKnows? - Evaluating Linked Data Heuristics with a Quiz that Cleans Up DBpedia In International Journal of Interactive Technology and Smart Education (ITSE), volume 8 pages 236-248, 2011 Emerald.
  3. Lina Wolf, Magnus Knuth, Johannes Osterhoff, und Harald Sack: RISQ! Renowned Individuals Semantic Quiz – A Jeopardy like Quiz Game for Ranking Facts In Proceedings of 7th International Conference on Semantic Systems (I-SEMANTICS 2011), of ICPS, Graz, Austria, 9 2011 ACM Press.
  4. Magnus Knuth, Nadine Ludwig, Lina Wolf, and Harald Sack: The Generation of User Interest Profiles from Semantic Quiz Games In Proceedings of the 2nd International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011), Athens, Greece, 9 2011.
  5. Nadine Ludwig, Jörg Waitelonis, Magnus Knuth, Harald Sack: WhoKnows? - Evaluating Linked Data Heuristics with a Quiz that Cleans Up DBpedia In Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011), of LNCS, Heraklion, Greece, 6 2011 Springer.