End-to-end solution for linked open data query logs analytics
Dihia Lanasri
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.