## 3 The Use of Personalized Ontologies for Query Expansion
### Approaches to Query Expansion Based on the Use of Personalized Ontologies
The _variable generality_ parameter, \(Variable_{Generality}\), is the number of subconcepts (_i.e._, children concepts) of \(c\) defined as \(Variable_{Generality}(x):=|SubConcepts(Type(x))|+1\), where the base value \(1\) allows to compute the value of ambiguity of the term \(x\) also when \(|SubConcepts(Type(x))|=0\). The _variable ambiguity_ parameter, \(Variable_{Ambiguity}(x,Q)\), is a number based on the relationships defined for the concepts identified for the query term \(x\) and based on the set of constraints related to \(x\) (for more details see [32]). A high value of _semantic ambiguity_ indicates that the sub-concepts of \(Type(x)\) could be good candidates to be added to the original query. The content-related ambiguity is considered when there is not a direct correspondence between the query terms and the ontology structure. This means that the query terms are not represented in the ontology, and then it is not possible to evaluate the semantic ambiguity of a query. The content-related ambiguity is defined by calculating the so-called _Neighbourhood_ of the query. The neighbourhood is a lattice defined by considering the relations between the queries' terms. This means that each query term is linked with the other terms written in the current user's query, and with the ones defined in past searches. After the user's query evaluation, each returned result is connected to each term in the query itself. This way a node of the lattice is a query term and a leaf is a result. When a user writes a new query, if the semantic ambiguity parameter cannot be computed, the lattice is analyzed in order to identify the set of results similar to the one obtained after the evaluation of the considered query. Two queries of a user are equivalent if their sets of results are equivalent. When the equivalent set of results has been identified in the lattice, it is then possible to locate the terms directly linked to these results. These terms are the candidates to be added to the original query. In [49] a technique to improve the retrieval effectiveness of a search engine on the Web is presented. This solution makes use of two profiles: \((1)\) a user's profile based on the user's search history, and \((2)\) a general profile learned from a category hierarchy. These profiles are used to overcome the impersonal behaviour of a search engine given the context of a user's query. A user's search history is represented by a tree model with the objective to capture the following information: queries, relevant documents, and related categories. In this tree model the root is a query, the nodes are the categories generated to classify the documents and the leaves are the relevant documents. The authors have assumed the use of search engines where the returned documents, after \begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Considered User’s Actions** & **Personal Ontology** \\ \hline
[32] & past user’s queries & defined from textual \\  & & information. \\
[49] & past user’s queries & based on the ODP \\ \hline
[50, 51] & preferred documents & defined from textual \\  & & information. \\
[53] & selection of terms from the ODP & based on the ODP \\ \hline \end{tabular}
\end{table} 
Table 1: Synthesis of the discussed methods for query expansion. the evaluation of a user's query, are classified into a set of categories (such as the Northern Light search engine). A document is considered relevant by a user if some of the following user behaviours are observed: a user selects it and reads it for a time slot, or a user saves/prints it. A user's profile is automatically learned from the user's search history, and it consists of a set of categories. For each category a weighted term vector is defined, where the term weight indicates the importance of the term for the category. A general profile is suitable for all users.