Result diversification deals with ambiguous or multi-faceted queries by providing documents that cover as many subtopics of a query as possible. Various approaches to subtopic modeling have been proposed. Subtopics have been extracted internally, e.g., from retrieved documents, and externally, e.g., from Web resources such as query logs. Internally modeled subtopics are often implicitly represented, e.g., as latent topics, while externally modeled subtopics are often explicitly represented, e.g., as reformulated queries.

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 in‐depth 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.

Analysis of existing methods for automatic optimization of link structures shows that these methods rely heavily on assumptions about the preferences and navigation behavior of users. Authors often do not state these assumptions explicitly and do not evaluate whether the assumptions are consistent with the actual behavior of the users of the site. This is a serious deficiency as experiments with simulated users show that incorrect assumptions can easily lead to inefficient link structures. In this work we present a framework that gives a systematic overview of alternative assumptions. On the basis of the framework we can select a set of assumptions that best matches the navigation behavior of the users in the site’s log files. We also present a method for optimizing hierarchical navigation menus on the basis of the selected assumptions. This method can be used interactively under full control of a web master. The system proposes modifications of the structure and explains why these modifications lead to more efficient menus. Evaluation by means of a case study shows that the modifications that are proposed effectively reduce the expected navigation time while preserving the coherence of the menu structure.