## 6 Demonstration and Results
### Results of Mapping the Concepts from a New Source to Existing Concepts in the Ubiquitous User Model  
First of all the system preprocess the source XML document to extract the labels of the terminal elements (elements which directly contain a value) where each label is a \(c_{s}\in C_{s}\). On the other side, the system takes into account all the concept labels consisting of lexical labeling, notation and documentation SKOS properties. Each label is a \(c_{t}\in C_{t}\). To maintain the readability of the example, for the XML source document, a sample of the first concept labels of the XML document and a partial view of relevant skos:prefLabels of the ubiquitous user model are shown in the results. In the entire experiment all concepts of XML document and all types of SKOS lexical labels are considered. The interoperability engine processes these concepts building a similarity matrix with the element level matching process described in 4.5.2. We present a partial view of similarity matrix in Table 3 showing just the most relevant concepts and collections. The first column represents eleven concepts of the source document that are \(c_{s}\in C_{s}\). Figure 6: Sample SOAP 1.2 request of LogWeight web service. The first row of Table 3 shows the skos:Collection names, in this case, Demographic data and general user data and Observations & Measurements. Each collection has several sub collections whose concepts are presented in the second row of Table 3. Only the most relevant collections and sub collections for the experiment and a sample of most relevant concepts are presented for readability. Each cell represents a result of the calculation of \(sim_{0}(c_{s}c_{t})\) described in Equation (1). Although the similarity measures are in fact categorized with criteria to determine the relation presented in 4.5.2, we chose to show the actual similarity values in Table 3.  
Some interesting similarity measures are highlighted for its analysis. If the cell is highlighted in red, it means that the concept label from the source is not expressive enough, so further disambiguation must be done in structure level matching process. A new concept might be added. If the cell is highlighted in yellow, this means that no clear _equivalence_ is found so far; it is a possible problem for this match. For instance, the similarity of _name vs. name_ seems equivalent but they are homonymous, because in the source it is the name of the exercise and in the target it refers to the person's name. Further disambiguation must be done in structure level matching process. The same stands for _calories_. In this case, we obtained two target exact matches and they refer to calories burned in exercise and daily calorie intake, but it is impossible to tell the difference with element level process. For the concept _duration_, we obtained that two source concepts can be _equivalent_ to the same target concept. If the cell is highlighted in green, it means that the _equivalence_ is very likely to be correct. From element level matching results the most related collection is calculated with Equation (3). Only the most relevant for the experiment collections of the ubiquitous user model are presented in Table 3: {_Demographic data and general user data, Observations & measurements_}. The observations & measurements collection has three sub collections: {_Health O&M, Physical activity & training, Food &Diet_}. The collection with higher relative frequency of membership of best suited concepts is the _Observations & measurements_ collection. We present, in detail, interesting results of some cases found in element level matching process. They correspond to the matching process for concepts _name_, _calories_ and _weight_. In the first step of structure level matching process, sets of neighbors are generated as described in Section 4.5.3.