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Semantic search log analysis: a method and a study on professional image search

 

Existing methods for automatically analyzing search logs describe search behavior on the basis of purely syntactic differences (overlapping terms) between queries. Although these statistics at a syntactic level 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. Recently, large amounts of semantic information have become publicly available in the form of linked data. In this paper we propose a method to exploit this information to enrich search queries with linked data entities 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 the commercial picture portal of a European news agency, which offers access to photographic images to professional users. 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 the more traditional syntactic way and using the newly 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 with a purely statistical approach that only considers term overlap, since queries related in the most frequent ways do not usually share terms. We discuss implications of our findings for improving log analysis, image collection management, and search engine design

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