With the rise of linked data and knowledge graphs, the need becomes compelling to find suitable solutions to increase the coverage and correctness of datasets, to add missing knowledge and to identify and remove errors. Several approaches – mostly relying on machine learning and NLP techniques – have been proposed to address this refinement goal; they usually need a partial gold standard, i.e. some “ground truth” to train automatic models. Gold standards are manually constructed, either by involving domain experts or by adopting crowdsourcing and human computation solutions.
Data generated as a side effect of game play also solves computational problems and trains AI algorithms. Annotated corpora of the size needed for modern computational linguistics research cannot be created by small groups of hand annotators. One solution is to exploit collaborative work on the Web and one way to do this is through games like the ESP game. Applying this methodology however requires developing methods for teaching subjects the rules of the game and evaluating their contribution while maintaining the game entertainment. In addition, applying this method to linguistic annotation tasks like anaphoric annotation requires developing methods for presenting text and identifying the components of the text that need to be annotated. In this paper we present the first version of Phrase Detectives (http: www.phrasedetectives.org), to our knowledge the first game designed for collaborative linguistic annotation on the Web.  The interoperability of data depends on the availability of alignments among different ontologies. Various approaches to match, merge and integrate ontologies and, more recently, to interlink RDF data sets were developed over. Even though the research area has matured, the full automation of the ontology alignment process is not feasible and the human user is indispensable. Such tasks involve mainly bootstrapping the underlying methods and for validating and enhancing their results. The question of acquiring such input still remains to be solved, in particular when it comes to the motivators and incentives that are likely to make people dedicate labor to ontology alignment tasks. In this paper we build on previous work of ours on using casual games to tackle this problem. We present SpotTheLink, the latest release of the OntoGame framework, which allows for the definition of mappings between Semantic Web ontologies as part of a collaborative game experience.We illustrate the idea of SpotTheLink in an instance of the game aiming to align DBpedia and PROTON, and explain the game background mechanics by which players’ inputs are translated into SKOS-based ontology mappings. A summary of findings of SpotTheLink user evaluation and the experiences we gained throughout the entire life span of OntoGame allow us to derive a number of best practices and guidelines for the design of incentives-minded semantic-content-authoring technology, in which human and computational intelligence are smoothly interwoven. Checking-in various venues in our surrounding environment via location-based apps like foursquare is becoming more and more popular, this behaviour makes people share some "bits" of their location with their friends. Exploiting this trend in a Human Computation fashion to collect information about urban environments is the aim of the Urbanopoly Android app -- a social, mobile and location-based Game with a Purpose designed around the idea of the "monopoly" board game. In this paper, we illustrate the main design choices for Urbanopoly -- including the use of social media like Facebook in the context of a Human Computation approach -- and we explain the game play. To realize the Smart Cities vision, applications can leverage the large availability of open datasets related to urban environments. Those datasets need to be integrated, but it is often hard to automatically achieve a high-quality interlinkage. Human Computation approaches can be employed to solve such a task where machines are ineffective. We argue that in this case not only people's background knowledge is useful to solve the task, but also people's physical presence and direct experience can be successfully exploited. In this paper we present UrbanMatch, a Game with a Purpose for players in mobility aimed at validating links between points of interest and their photos; we discuss the design choices and we show the high throughput and accuracy achieved in the interlinking task. This article describes how a semantic search engine has been build from, and still is continuously improved by, a semantic analysis of the “footprints” left by players on the gaming Web platform ARTigo. The Web platform offers several Games With a Purpose (GWAPs) some of which have been specifically designed to collect the data needed for building the artwork search engine. ARTigo is a “tagging ecosystem” of games that cooperate so as to gather a wide range of information on artworks. The ARTigo ecosystem generates a folksonomy saved as 3rd-order tensor, that is a generalization of a matrix, the three orders or dimensions of which represent (1) who (2) tagged an (3) an artwork. The semantic search engine is build using a non-trivial generalization of the well-known, matrix-based, Latent Semantic Analysis (LSA) methods and algorithms. ARTigo is in service for five years and is subject to an active research constantly resulting in new developments, some of which are reported about for the first time in this article. While associations between concepts in our memory have different strengths, explicit strengths of links (edge weights) are missing in Linked Data. In order to build a collection of such edge weights, we created a web-game prototype that ranks triples by importance. In this paper we briefly describe the game, Linked Data preprocessing aspects, and the promising results of an evaluation of the game. Weaving the semantic Web requires that humans contribute their labor and judgment for creating, extending, and updating formal knowledge structures. Hiding such tasks behind online multiplayer games presents the tasks as fun and intellectually challenging entertainment. The games we've presented are the first prototypes of the OntoGame series. We're extending and improving the scenarios in several directions. Purpose – Linking Open Data (LOD) provides a vast amount of well structured semantic information, but many inconsistencies may occur, especially if the data are generated with the help of automated methods. Data cleansing approaches enable detection of inconsistencies and overhauling of affected data sets, but they are difficult to apply automatically. The purpose of this paper is to present WhoKnows?, an online quiz that generates different kinds of questionnaires from DBpedia data sets.Design methodology approach – Besides its playfulness, WhoKnows? has been developed for the evaluation of property relevance ranking heuristics on DBpedia data, with the convenient side effect of detecting inconsistencies and doubtful facts.Findings – The original purpose for developing WhoKnows? was to evaluate heuristics to rank LOD properties and thus, obtain a semantic relatedness between entities according to the properties by which they are linked. The presented approach is an efficient method to detect popular prop...