## 3 The Use of Personalized Ontologies for Query Expansion
### Approaches to Query Expansion Based on the Use of Personalized Ontologies
The reason for using this additional information is that the knowledge acquired from a user is limited only to his/her previous requests, and thus it might not be sufficient to determine the appropriate context for a query by having new user's interests. The considered general knowledge for defining this profile is provided by the ODP. In particular, only the first three levels of the ODP's structure are taken into account to represent the set of all categories. The personalized search is achieved by mapping a user's query to a set of categories (obtained after the analysis of the user and general profile) that define the query context. After this, a similarity value between the user's query and the relative set of categories is calculated; next the categories are ranked in descending order of similarity. The top three categories are visualized to the user who can select the best ones for his/her actual search. If the user's interests are not among these three top categories, then the system provides the next three ones, and so on. The query is initially submitted without specifying any category. Then, the query is again submitted by adding the selected categories to the initial query. These two lists of returned documents are merged by adopting a weighted voting-based merging algorithm; the combined list will be visualized to the user. [PERSON] _et al._[50] have defined an algorithm aimed at disambiguating short user's queries. In the considered application the users have to locate e-learning material on the Internet. In this work the domain ontology has been semi-automatically defined by combining a domain vocabulary with the relevant concepts extracted from the preferred e-learning material. This information is stored in the repository after search sessions performed by users (e.g., students or teachers). A user is guided in locating the learning objects (e.g., books of interest) on the Internet by using titles, descriptions and other attributes. The core of this approach is to identify the portion of knowledge related to the user's query in the considered domain ontology. The selected part of knowledge is a sub-tree of the ontology that the authors have called \"User Intention Tree\" (UIT). Thus, the UIT represents the user's interests that identifies the so-called _user intention_ related to a given query. The original query is then expanded with the concepts of the UIT. In the ontology each concept \(c\) is described by a set of keywords and their synonyms, called _base concept_, \(BC\), of \(c\). Then for a given user's query \(Q\) the related UIT is identified by finding the right \(BC\)'s base concepts. This phase is performed by assigning a score, (called the base concept score, \(BCS\)), to each concept as: \(BCS(c_{i})=\frac{|BC_{i}\cap Q|}{|BC_{i}|}\)\(\forall c_{i}\in C\), where \(C\) is the set of concepts of the ontology. The score of a selected concept \(c_{i}\) is defined as the number of keywords and synonyms that match \(Q\) (user query keywords) normalized by the total number of keywords and synonyms of concept \(c_{i}\). For each pair of concepts directly linked in the ontology, with both having \(BCS>0\), a new score called weighted base concept score, \(WBCS\), is calculated as: \(WBCS(c_{i},c_{k})=BCS(c_{i})\cdot BCS(c_{k})\), where \(c_{i}\) is the considered concept and \(c_{k}\) is a concept directly connected with it having \(BCS(c_{k})>0\). Then, the propagated base concept score, \(PBCS\) for a concept \(c_{i}\) is defined as the sum over \(WBCS\)s. Afterthese phases, several sub-trees of the ontology (with weighted relations and concepts) are located. The last phase concerns the construction of the UIT. The several disjoint parts of the ontology (sub-trees) have to be aggregated to define the UIT. The aggregation process is defined by finding the common node containing a maximum conjunction of the base concepts from the sub-trees. As previously stated, the UIT concepts are added to the original query, but a great number of candidates can be obtained.