BetterRelations: Collecting Association Strengths for Linked Data Triples with a Game

Jörn Hees; Thomas Roth-Berghofer; Ralf Biedert; Benjamin Adrian; Andreas Dengel
In: Stefano Ceri; Marco Brambilla (Hrsg.). Search Computing - Broadening Web Search. Pages 223-239, Lecture Notes in Computer Science (LNCS), Vol. 7538, ISBN 978-3-642-34212-7, Springer, 2012.


The simulation of human thinking is one of the long term goals of the Artificial Intelligence community. In recent years, the adoption of Semantic Web technologies and the ongoing sharing of Linked Data has generated one of the world's largest knowledge bases, bringing us closer to this dream than ever. Nevertheless, while associations in the human memory have different strengths, such explicit association strengths (edge weights) are missing in Linked Data. Hence, finding good heuristics which can estimate human-like association strengths for Linked Data facts (triples) is of major interest to us. In order to evaluate existing approaches with respect to human-like association strengths, we need a collection of such explicit edge weights for Linked Data triples. In this chapter we first provide an overview of existing approaches to rate Linked Data triples which could be valuable candidates for good heuristics. We then present a web-game prototype which can help with the collection of a ground truth of edge weights for triples. We explain the game's concept, summarize Linked Data related implementation aspects, and include a detailed evaluation of the game.



Weitere Links