ARTigo: Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis

2013-11-10

Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 1

Volume

Vol. 1 (2013): First AAAI Conference on Human Computation and Crowdsourcing

Disco: Human and Machine Learning in Games Workshop

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.

10.1609/hcomp.v1i1.13060