Gianluca Demartini

TL;DR: In this article , the authors investigate the impact of human metadata (e.g., what other human assessors think of the current document, as in which relevance level has already been selected by the majority crowd workers), machine metadata (i.e., how IR systems scored this document, such as its average position in ranked lists, statistics about the document such as term frequencies), and cost, as well as how metadata quality positively or negatively impact the collected judgments.

TL;DR: An innovative Desktop search solution, which relies on extracted metadata, context information as well as additional background information for improving Desktop search results and a practical application of this approach | the extensible Beagle ++ toolbox is presented.

TL;DR: In this paper, the authors study whether crowdsourcing is an effective and reliable method to assess truthfulness during a pandemic, targeting statements related to COVID-19, thus addressing (mis)information that is both related to a sensitive and personal issue and very recent as compared to when the judgment is done.

TL;DR: In this paper , the authors address the problem of optimizing human intelligence task construction by providing its formal definition and by applying a local search method initialized by a greedy construction to solve it, and experimentally show the flexibility of the proposed solution in addressing different type of constraints, both on the item and on the worker side.

TL;DR: This paper proposes new techniques to improve the correctness of domains and ranges by identifying the cases in which a property is used in the data with several dierent semantics, and resolving them by updating the underlying schema and/or by modifying the data without compromising its retro-compatibility.