End-to-end solution for linked open data query logs analytics
This work addresses the problem of analyzing LOD query-logs for users in data analytics and decision-making domains, but it appears incremental as it builds on existing log exploitation methods.
The authors tackled the challenge of extracting valuable information from Linked Open Data (LOD) query-logs, which are complex and suffer from quality and provenance risks, by proposing an end-to-end solution and validating it with real logs and experiments.
Important advances in pillar domains are derived from exploiting query-logs which represents users interest and preferences. Deep understanding of users provides useful knowledge which can influence strongly decision-making. In this work, we want to extract valuable information from Linked Open Data (LOD) query-logs. LOD logs have experienced significant growth due to the large exploitation of LOD datasets. However, exploiting these logs is a difficult task because of their complex structure. Moreover, these logs suffer from many risks related to their Quality and Provenance, impacting their trust. To tackle these issues, we start by clearly defining the ecosystem of LOD query-logs. Then, we provide an end-to-end solution to exploit these logs. At the end, real LOD logs are used and a set of experiments are conducted to validate the proposed solution.