Abstract
Existing methods for automatically analyzing search logs describe search behavior on the basis of syntactic differences (overlapping terms) between queries. Although these analyses provide valuable insights into the complexity and successfulness of search interactions, they offer a limited interpretation of the observed searching behavior, as they do not consider the semantics of users' queries. In this article we propose a method to exploit semantic information in the form of linked data to enrich search queries so as to determine the semantic types of the queries and the relations between queries that are consecutively entered in a search session. This work provides also an inβdepth analysis of the search logs of professional users searching a commercial picture portal. Compared to previous image search log analyses, in particular those of professional users, we consider a much larger dataset. We analyze the logs both in a syntactic way and using the proposed semantic approach and compare the results. Our findings show the benefits of using semantics for search log analysis: the identified types of query modifications cannot be appropriately analyzed by only considering term overlap, since queries related in the most frequent ways do not usually share terms.