# Semantic Search Enhanced with Rating Scores
## 1 Introduction  
The most significant improvement within the Semantic Web research area pertains to reasoning and searching abilities. In this perspective, semantic similarity reasoning, which relies on the knowledge coded in a reference ontology [1], is a different technique with respect to the well-known deductive reasoning used in expert systems. In [2], we proposed SemSim, a semantic search method based on a _Weighted Reference Ontology (WRO)_. In _SemSim_, both the resources in the search space and the requests of users are represented by means of an _Ontology Feature Vector_ (\(OFV\)), which is a set of concepts from the _WRO_. We distinguish the user request, also denoted as _Request Vector_ (\(RV\)), from the description of a resource, also referred to as _Autnotation Vector_, indicated by \(AV\). In the search process, _SemSim_ contrasts the \(RV\) against each \(AV\), and the result is a ranking of the resources that exhibit the highest similarity degree with respect to the request defined by the user. In [2], we analyzed two different approaches in order to weigh the reference ontology, namely the frequency-based and the uniform probabilistic approaches. In the experiment described in that paper, we show that _SemSim_ by the _frequency_-based approach outperforms the _SemSim_ by the _uniform probabilistic_ approach, as well as the most representative similarity methods from the literature. In this work, we present a new method, referred to as _SemSim_\({}^{e}\). It relies on the frequency-based approach and revises _SemSim_ along two directions. According to the first direction, in contrasting the \(RV\) with the \(AV\), _SemSim_\({}^{e}\) takes into consideration the cardinality of the set of the concepts (features) in the user request rather than the maximal cardinality of the compared \(OFV\). This choice allows us to give more relevance to the features which are requested by the user rather than the extra features contained in the annotation vectors available in the search space. Along the second direction, _SemSim_ has been enhanced with the rating scores _High_ (\(H\)), _Medium_ (\(M\)), and _Low_ (\(L\)) in the \(OFV\), with regard to both the request and the search space resources. Within the request, rating scores denote the preferences given by the user to the concepts of the WRO used to specify the query whereas, within the annotation vectors, rating scores represent the levels of quality associated with the concepts when they describethe resources. Consider an example rooted in the tourism domain, where the user is searching for a vacation package by specifying the following features: \(InternationalHotel\) (\(H\)), \(LocalTransportation\) (\(M\)), \(Cultural\)_Activity_ (\(H\)), and \(Entertainment\) (\(L\)). On the basis of the given rating scores, he/she gives a high preference to resorts which are international hotels offering cultural activities, and less priority to the remaining features, in particular to the entertainments. Analogously, a holiday package annotated with \(HorseRiding\) (\(H\)), \(Museum\) (\(M\)), and \(ThaiMeal\) (\(L\)) is characterized by a high quality level with regard to the horse riding service, rather than the facilities in visiting museums or having Thai meals at lunch or dinner. Note that, in [3], a proposal concerning rating scores was given, where the concepts of the \(WRO\) are weighted according to the _uniform probabilistic_ approach [4], rather than the \(frequency\)-based one. Furthermore, in our approach we assumed that, given a facility (for instance, \(HorseRiding\)) included in a tourist package, the higher the user's priority about that facility, the higher the expectancy about the quality of the same facility and, therefore, the greater the availability of the user for considering more expensive solutions. In this paper, we have experimented \(SemSim^{e}\) in the domain of tourism and we have compared it to the \(SemSim\) method defined in [2] and a further evolution of \(SemSim\), referred to as \(SemSim_{RV}\).