## 3 The Use of Personalized Ontologies for Query Expansion
### Approaches to Query Expansion Based on the Use of Personalized Ontologies  
Query expansion aims to improve the user's Web search in order to retrieve more relevant documents. In this section, we review some approaches to query expansion where an ontology is semi-automatically defined on the basis of the observation of the user's behaviour during Web searches. In the literature, a limited number of such contributions can be found. In [32, 49] query expansion strategies which consider both ontology and past user's queries are presented. In [32] the authors define a graph where each node is a query term, and with each term the related list of Web results is associated. This graph is updated each time a user's query is performed. Details on how to handle the exponential grown of this structure are not indicated in the paper; in fact, problems related to time and space complexity can arise. In [49], past user's searches are used to define a user's profile, and the ODP is used to define a general profile. These two profiles are then combined to establish the context of the user's query to personalize the current user's request. The authors assert that good results have been obtained with the defined strategy, but experimental results are performed only with seven users, a few hundred queries, and a limited number of relevant documents. Whereas in [32] past user's queries are involved only if there is not a direct correspondence between a user's query and the ontology, in [49] they are both jointly considered to better define the search context. In [50, 51] local methods to expand the user's query including positive relevance feedback strategies are presented. Both these works define ontology-based query expansion methodologies applied in the e-learning context. Although [50] and [51] have the same goal, the first method is more complex from a computational point of view. Both methods define the user's ontology based on relevant terms extracted from e-learning material stored by the users. However, details on how this process is performed are missing. In the literature, the semi (or automatic) definition of a ontology from textual information is a challenging research topic [52]. As reported in Section 2 works that combine hybrid methodologies between query expansion and re-ranking phase are generally preferred. Thus, in this section we also discuss the approach defined in [53] where a hybrid combination of query expansion strategy for improving the re-ranking of Web results is presented. The terms to add to the query are directly selected from the ODP by the user, and the ODP is also used to extract the relevant concepts from the documents. In Table 1 a summary of the above approaches is reported. In the following the papers previously introduced are more extensively explained. In [32] the authors have defined a method for query expansion based on the use of ontologies. The use of an ontology reduces the possible (mis)interpretations of a query, especially when a user writes short queries. The authors have defined the concept of _ambiguity_ as the gap between a user's query and his/her information need. In order to reduce this gap two types of ambiguity related to a query have been modelled: (i) _semantic ambiguity_, and (ii) _content-related ambiguity_. The semantic ambiguity of a query can be evaluated when the query terms belong to the domain ontology. This means that a term \(x\) written in the query \(Q\) is associated with a concept \(c\) of the ontology, (_i.e._, \(c\) is defined as \(Type(x)\)), and its relationships. The semantic ambiguity of a query \(Q\) is then defined as: \(SemanticAmbiguity(Q)=\sum_{x\in Q}Variable_{Generality}(x)\cdot Variable_{ Ambiguity}(x,Q)\), where \(x\) is a query term.