## 3 The Use of Personalized Ontologies for Query Expansion  
The aim of _query expansion_ is to refine the user's query by adding new meaningful terms to the initial query in order to more effectively express the user's needs. The query refinement can be made either automatically or with the help of the user. In the literature, two main classes of methods for query expansion have been proposed [25]: _global_ and _local_ methods. **Global methods** are independent from both the query and the results returned by the query evaluation. They include query expansion by using thesauri or the WordNet and query logs (particularly appropriate for Web search). With the support of a thesaurus each term \(t\) can be expanded with synonyms and words related to \(t\). Thesaurus-based query expansion has the advantage of not requiring any user input, and generally it increases recall but decreases precision, especially when the query contains ambiguous terms [25]. [PERSON] [26] is one of the pioneer researchers facing the problem of how the meaning of a phrase can change when one of the words is replaced by a similar word. **Local methods** expand the query based on the information found in the result set produced by the initial query evaluation. They include relevance feedback (positive or negative relevant documents explicitly indicated by the user), pseudo-relevance feedback (or blind relevance feedback), and (global) indirect relevance feedback techniques. With relevance feedback the user indicates some of the results obtained after a first search as relevant or non-relevant. From the relevant results the significant concepts are extracted and added to the original query in order to refine the Web search. In pseudo-relevance feedback it is assumed that the top \(k\) ranked documents are relevant, and the relevance feedback is performed under this assumption. Instead, in the indirect relevance feedback _indirect_ sources are used as the basis for relevance feedback. The area of research called _clickstream mining_[25] is a case study of indirect relevance feedback, and it is widely used on the Web [27, 28]. If a user clicks on the links included on the Web pages, then it is assumed that those pages as relevant to him/her. When using an ontology the concepts contained in it can be used for word sense disambiguation and subsequent query expansion. Ontologies offer a global technique of query expansion. The idea is to add terms of the ontology that are semantically related to query terms. During the query expansion phase a crucial problem is to select the number of terms that have to be added to the query in order to better satisfy the user request [29]. What is the right number of candidate terms in the query expansion process? Two points of views are considered from researchers: a massive approach and a qualitative approach. In the first case, more than 20 terms can be added to the original query. [PERSON] [30] has indicated this methodology as the most suited one for routing the query vector towards the centroid of relevant documents. Whereas in the second case, 3-4 terms per query are usually inserted. Although a precise number is not indicated, the optimal number varies from query to query. The idea is that the number is less important than the quality of the selected terms [31]. In this scenario, ontologies can be used as a good support to the qualitative approach. The objective is to identify the right relationships in the user's ontology and to expand the queries with the right concepts. Once a query term has been located in the ontology structure, two ways of navigation are possible in order to expand it: (1) broader navigation and (2) narrower navigation. By adopting the first approach a semantic generalization is considered, whereas with the second one a semantic specialization is analyzed. When using these techniques, the problem is to halt the expansion to the right level in the hierarchy [32, 33] in order to avoid a massive approach.