Enabling community-driven information integration through clustering

Abstract

It has become widely recognized that user feedback can play a fundamental role in facilitating information integration tasks, e.g., the construction of integration schema and the specification of schema mappings. While promising, existing proposals make the assumption that the users providing feedback expect the same results from the integration system. In practice, however, different users may anticipate different results, due, e.g., to their preferences or application of interest, in which case the feedback they provide may be conflicting, thereby deteriorating the quality of the services provided by the integration system. In this paper, we present clustering strategies for grouping information integration users into groups of users with similar expectations as to the results delivered by the integration system. As well as grouping information integration users, we show that clustering results can be used as inputs to a wide range of functionalities that are relevant in the context of crowd-driven information integration. Specifically, we show that clustering can be used to identify feedback of relevance to a given user by exploiting the feedback provided by other users in the same cluster. We report on evaluation exercises that assess the effectiveness of the clustering strategies we propose, and showcase the benefits communityand crowd-driven information integration can derive from clustering.

Crowdsourcing for data management

“…For example, Nguyen et al [77] study how to leverage answers provided by expert (and costly) workers to evaluate random workers. There has also been work on identifying when users can share subjective feedback, by clustering workers with similar perspectives [8], but again there is a need to generalise such work beyond a specific case study. Additional research has investigated topics such as modeling the skills of crowd members [112], the identification of experts within social networks [13], and the identification of collections of crowd workers that together are most likely to generate a correct outcome [16].…”