  The explosion in the size and the complexity of the available Knowledge
Graphs on the web has led to the need for efficient and effective methods for
their understanding and exploration. Semantic summaries have recently emerged
as methods to quickly explore and understand the contents of various sources.
However in most cases they are static not incorporating user needs and
preferences and cannot scale. In this paper we present iSummary a novel
scalable approach for constructing personalized summaries. As the size and the
complexity of the Knowledge Graphs for constructing personalized summaries
prohibit efficient summary construction, in our approach we exploit query logs.
The main idea behind our approach is to exploit knowledge captured in existing
user queries for identifying the most interesting resources and linking them
constructing as such highquality personalized summaries. We present an
algorithm with theoretical guarantees on the summarys quality linear in the
number of queries available in the query log. We evaluate our approach using
three realworld datasets and several baselines showing that our approach
dominates other methods in terms of both quality and efficiency.
