Query Log Mining in Search Engines

The Web is a huge read-write information space where many items such as documents, images or other multimedia can be accessed. In this context, several information technologies have been developed to help users to satisfy their searching needs on the Web, and the most used are search engines. Search engines allow users to find Web resources formulating queries (a set of terms) and reviewing a list of answers. One of the most challenging goals for the Web community is to design search engines that allow users to find resources semantically connected to their queries. The huge size of the Web and the vagueness of the most commonly used terms to formulate queries still poses a huge problem to achieves this goal. In this thesis we propose to explore the user’s clicks registered in the search engine logs in order to learn how users search and also in order to design algorithms that could improve the precision of the answers suggested to users. We start by exploring the properties of the user’s click data. This exploration allows us to determine the sparse nature of the data providing users behavior models that help us to understand how users search in search engines. Secondly, we will explore the user’s click data to find useful associations among queries registered in the logs. We will focus the efforts on the design of techniques that will allow users to find better queries than the original query. As an application, we will design query reformulation methods that will help users to find more useful terms in order to represent their needs. On using document terms we will build vectorial representations for queries. By applying clustering techniques we are able to compute clusters of similar queries. Using query clusters, we provide techniques for document and query suggestions which allows us to improve the precision of the answer lists. Finally we will design query classification techniques that allow us to find concepts semantically related with the original query. In order to do this, we classify the users’s queries into a Web directory. As an application, we provide methods for the maintenance of the directory.