Nowadays, companies are racing towards Linked Open Data (LOD) to improve their added value, but they are ignoring their SPARQL query logs. If well curated, these logs can present an asset for decision makers. A naive and straightforward use of these logs is too risky because their provenance and quality are highly questionable. Users of these logs in a trusted way have to be assisted by providing them with in-depth knowledge of the whole LOD environment and tools to curate these logs. In this paper, we propose an interactive and intuitive trust based tool that can be used to curate these LOD logs before exploiting them. This tool is proposed to support our approach proposed in our previous work Lanasri et al. [2020].

In big data Era, significant advances in e-commerce, targeted marketing, social shopping, e-tourism, etc. are derived basically from collective intelligence. Such applications mainly exploit data generated by users to extract different valuable information. User content represents data, information, or media content voluntarily provided by people Krumm et al. [2008], when they interact with web sites, social media, and data sources, etc. This data regroups social data, YouTube videos, blogs and micro-blogs, query-logs, etc. Analysis of this data provides useful information helping to understand user behavior, user opinions, topics of interest, etc. It helps to detect hidden patterns and to construct users' profiles, in order to propose user-centric solutions like: recommendation systems, content personalization, cache improvement, etc. for successful user experience.