Exploiting the playfulness of games has been extremely successful in bringing humans “in the loop” to solve com­plex computational tasks that would otherwise be hardly tractable. Although many proposals and systems after this paradigm have been developed, deployed, and tested, the relationship between play and human computation still de­serves more investigations. Most work in human computa­tion focuses on the ability for the machine to exploit, or learn from, humans. The workshop has a slightly different focus: the exploration of extending “I learn” (“disco” in Latin) to machines and humans alike. Games hold tremen­dous potential for discovery related to human and machine computation because of the intrinsic relation between play and learning. Extending and building upon the focus of past workshops on games and human computation Disco aims at exploring the intersection of entertainment, learning and human computation.

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.

The ESP Game tends to generate "low effort" or "surface semantics" tags. This paper presents two variations of the ESP Games called "squaring" and "scripting" that trim the ESP Game to collect "deep semantics" tags. The approaches do not require players to get used to, and for the GWAP operators to deploy, new games. First experiments point to the efficiency of squaring and scripting the ESP Game at collecting "deep semantic" tags.