# Semantic Search Enhanced with Rating Scores
## 4 SemSim\({}^{e}\) Evaluation  
In the experimentation, we asked 15 colleagues at work to identify their privileged tourist packages (request vector, _rv_) by choosing 4 or (at most) 5 concepts of the ontology illustrated in Figure 1. In particular, assume the user desires to reside in an international hotel, in a location connected by flight and local transportation services, and the opportunity of enjoying cultural activities and entertainments. The related request vector is given below:  
\[rv=\left(InternationalHotel,\,LocalTransportation,\,CulturalActivity,\,Entertainment,\,Flight\right)\]  
By associating a score among \(H\), \(M\), and \(L\) with each concept in the _rv_, the user can indicate the level of his/her priority for that concept. Accordingly, the user can express his/her priority in the above _rv_ by the following:  
\[rv^{e}=\left(InternationalHotel\,\,(H),\,LocalTransportation\,\,(M),\,CulturalActivity\,\,(H),\,Entertainment\,\,(L),\,Flight\,(H)\right)\]  
where _InternationalHotel_, _CulturalActivity_ and _Flight_ received a higher preference with respect to _LocalTransportation_ and _Entertainment_, the latter with the lowest score. Then, we requested our colleagues to examine the 10 packages reported in Table 2, where each concept has been associated with a score among \(H\), \(M\), and \(L\), specifying, similar to _TripAdvisor_, the quality level of the offered services. Our colleagues were also asked to choose 5 packages, out of 10, more similar to their _rv_, and to assign a score to each chosen one representing the degree of similarity between the package and their _rv_ (Human Judgment--HJ). The request vectors defined by the colleagues are shown in Table 3. \begin{table}
\begin{tabular}{c|l} \hline \hline _Packages_ & _AV\({}^{e}\)_ \\ \hline P1 & (Camppsite (\(H\)), LocalTransportation (\(H\)), Concert (\(M\)), Flight(\(M\)), \\  & ArcheologicalSite (\(H\)), Food (\(L\)) \\ P2 & (FarmHouse (\(H\)), Trekking (\(M\)), HorseRiding (\(H\)), MediterraneanMeal (\(H\))) \\ P3 & (InternationalHotel (\(M\)), LocalTransportation (\(M\)), ArcheologicalSite (\(H\)), \\  & Flight (\(H\)), RegularMeal (\(M\)) \\ P4 & (InternationalHotel (\(H\)), CarRental (\(H\)), Museum (\(H\)), Cinema (\(L\)), Flight (\(H\)), \\  & InternationalMeal (\(L\)), ArtGallery (\(H\)), ShoppingCenter (\(L\))) \\ P5 & (Bed\& Breakfast (\(M\)), LocalTransportation (\(H\)), Train (\(H\)), RockConcert(\(H\)), \\  & Cinema (\(M\)), BikeRiding (\(L\))) \\ P6 & (Bed\& Breakfast (\(H\)), Bazaar (\(H\)), Museum (\(M\)), Theater (\(M\)), \\  & OpenAirActivity (\(L\))) \\ P7 & (SeasideCottage (\(M\)), Riding (\(H\)), Boating (\(H\)), Train (\(M\)), VegetarianMeal (\(H\))) \\ P8 & (Pension (\(M\)), CarRental (\(H\)), Shopping (\(L\)), CulturalActivity (\(H\)), LightMeal L) \\ P9 & (Camppsite (\(H\)), LocalTransportation (\(M\)), OpenAirActivity (\(H\)), Train (\(M\)), \\  & VegetarianMeal (\(H\))) \\ P10 & (Pension (\(M\)), ArtGallery (\(H\)), EthnicMeal (\(H\)), ClassicalConcert (\(H\)), \\  & LocalTransportation (\(L\)), Entertainment (\(L\))) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tourist packages. For the purpose of illustration, let us consider the request vector defined by the user \(U3\). This user desires to get to his/her destination by flight, reside in an international hotel, commute by public transportation, enjoy international meals and local attractions. As we observe, in the request vector of \(U3\) all the features have been refined by the score \(H\), except \(Flight\) which has been refined by \(M\). Table 4 shows the experimental results related to the user \(U3\). The first column contains the packages chosen by the user, that are \(P1\), \(P3\), \(P4\), \(P5\), \(P8\), and, respectively, the related similarity values 0.95, 0.8, 0.5, 0.4, 0.2. The other columns illustrate the 10 packages ranked, respectively, according to \(SemSim\), \(SemSim_{RV}\) and \(SemSim^{e}\), with the associated similarity values with \(U3\).