  Purpose: Advanced usage of Web Analytics tools allows to capture the content
of user queries. Despite their relevant nature, the manual analysis of large
volumes of user queries is problematic. This paper demonstrates the potential
of using information extraction techniques and Linked Data to gather a better
understanding of the nature of user queries in an automated manner.
  Design/methodology/approach: The paper presents a large-scale case-study
conducted at the Royal Library of Belgium consisting of a data set of 83 854
queries resulting from 29 812 visits over a 12 month period of the historical
newspapers platform BelgicaPress. By making use of information extraction
methods, knowledge bases and various authority files, this paper presents the
possibilities and limits to identify what percentage of end users are looking
for person and place names.
  Findings: Based on a quantitative assessment, our method can successfully
identify the majority of person and place names from user queries. Due to the
specific character of user queries and the nature of the knowledge bases used,
a limited amount of queries remained too ambiguous to be treated in an
automated manner.
  Originality/value: This paper demonstrates in an empirical manner both the
possibilities and limits of gaining more insights from user queries extracted
from a Web Analytics tool and analysed with the help of information extraction
tools and knowledge bases. Methods and tools used are generalisable and can be
reused by other collection holders.
