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

arXiv:2403.06016v1 Announce Type: cross
Abstract: 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 …

abstract advances analytics arxiv cs.cl cs.db data decision domains extract influence information knowledge logs making query solution type understanding work