We present WiBi, an approach to the automatic creation of a bitaxonomy for Wikipedia, that is, an integrated taxonomy of Wikipage pages and categories. We leverage the information available in either one of the taxonomies to reinforce the creation of the other taxonomy. Our experiments show higher quality and coverage than state-of-the-art resources like DBpedia, YAGO, MENTA, WikiNet and WikiTaxonomy. WiBi is available at http://wibitaxonomy.org.

We present MultiWiBi, an approach to the automatic creation of two integrated taxonomies for Wikipedia pages and categories written in different languages. In order to create both taxonomies in an arbitrary language, we first build them in English and then project the two taxonomies to other languages automatically, without the help of language-specific resources or tools. The process crucially leverages a novel algorithm which exploits the information available in either one of the taxonomies to reinforce the creation of the other taxonomy. Our experiments show that the taxonomical information in MultiWiBi is characterized by a higher quality and coverage than state-of-the-art resources like DBpedia, YAGO, MENTA, WikiNet, LHD and WikiTaxonomy, also across languages. MultiWiBi is available online at http://wibitaxonomy.org/multiwibi

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 conceptconcept 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.

In this demonstration we present WoSIT, an API for Word Sense Induction (WSI) algorithms. The toolkit provides implementations of existing graph-based WSI algorithms, but can also be extended with new algorithms. The main mission of WoSIT is to provide a framework for the extrinsic evaluation of WSI algorithms, also within end-user applications such as Web search result clustering and diversification.