Validating And Extending Semantic Knowledge Bases Using Video Games With A Purpose

Large-scale knowledge bases are important assets in NLP. Frequently, such resources are constructed through automatic mergers of complementary resources, such as WordNet and Wikipedia. However, manually validating these resources is prohibitively expensive, even when using methods such as crowdsourcing. We propose a cost-effective method of validating and extending knowledge bases using video games with a purpose. Two video games were created to validate concept-concept and concept-image relations. In experiments comparing with crowdsourcing, we show that video game-based validation consistently leads to higher-quality annotations, even when players are not compensated.