## 3 The Use of Personalized Ontologies for Query Expansion
### Approaches to Query Expansion Based on the Use of Personalized Ontologies
In order to avoid a massive approach for the query expansion phase, two stages are defined for pruning the nodes. (1) In this step the degree of correlation between a concept \(c_{i}\) (having \(BCS(c_{i})>0\)), and its parent concept \(j\) is defined. For each \(c_{j}\) the total impact score (\(TIS\)) parameter is calculated as the sum of the impact scores, \(IS\)s, of all subconcepts (direct and indirect) having not null \(BCS\) value, _i.e._, \(TIS(c_{j})=\sum IS(c_{i},c_{j})\). The impact score between the parent node and one of its child nodes is defined as: \(IS(c_{i},c_{j})=\{PBCS(c_{i})\cdot\frac{number\,of\,all\,descendants\,of\,c_{i} }{number\,of\,all\,descendants\,of\,c_{j}}\}\). If the concept is a leaf then the number of descendents is set to \(1\). The \(TIS\) value of each parent node \(j\) is compared with a threshold value \(\gamma\) to limit the upper bound of a minimal UIT. (2) If numerous nodes are obtained after the first stage, then a semantic distance between UIT concepts is defined to limit the expansion level. A threshold value is assumed to discard the concepts with a low distance value. This approach could be onerous with respect to the computational time if the ontology is made up of a large number of concepts. In e-learning systems users have to be able to easily discover the relevant information which supports learning activity. To enhance the performance of the searching task, query expansion may be used. In [51] a query expansion methodology based on a semantic user model that represents individual user's interests has been presented. A user model is exploited to represent the interests and background knowledge of individual learners [54]. Formally, a user model \(U_{model}=(UP,IC)\) consists of a set \(UP\) of user's identity information (such as user's name, age, education level, learning orientation,...), and a set \(IC\) describing user's interests. The representation and the construction of \(IC\) is the main part of \(U_{model}\). In the considered e-learning system each resource is annotated with metadata to describe its content, and a domain ontology is used to represent the concepts, instances and relationships from those annotations. The \(IC\) set is defined by considering the \(k\) resources selected by a user. From the obtained \(k\) resources the metadata are extracted, and put together with the keywords defined by a user to formulate his/her query. Formally we have \(W=UK\cup MD\), where \(UK\) is the set of the keywords from the user's query, and \(MD\) is the set of all the metadata annotations from the \(k\) resources. For each \(w_{i}\in W\) the corresponding set of synonyms, \(SYN\), is obtained by analyzing a specific domain lexicon. From \(SYN\) the related concepts, instances and relationships of the domain ontology are extracted to construct the set of user's interests \(IC\). Once the \(IC\) set is defined, the process of query expansion is obtained in several steps: (1) from each query term its synonyms are identified in a specific domain lexicon, (2) the query terms and their synonyms are used to mine concepts, instances and relationships from the domain ontology, (3) the extracted ontology obtained from point (2) is compared with the specific user model \(IC\) to identify the interests related to the current query, (4) a similarity measure is used to arrange the elements extracted from \(IC\) in descending order of corresponding interest relevance degree, and (5) the top \(k\) concepts (or instances) are added to the query. Evaluations have shown good results by comparing documents obtained with and without the query expansion strategy. Furthermore, the behaviour of two users with the same queries but having a different user model \(IC\) has been compared. Evaluationshave produced a different set of returned search results, thus providing a personalized service in the e-learning system. If two people are searching for _raptors_ they may be looking at different contexts (prehistoric animals or basketball teams), but they will obtain the same set of results.