## 4 Experiments
### Results  
With a complete set of triplets which resulted from the process above, we evaluated the level of correlation among the three documents. The results are shown in Table 5. The correlation of concepts in the semantic map is shown in bold fonts above the main diagonal while the correlation for the relationships among concepts is shown below the main diagonal in italic fonts. These results show that the concepts are more correlated than the relationships. For example, the correlation of concepts between the US Smart City fact sheet and Philadelphia Smart City Road Map documents is very high at 0.67 which is expected since Philadelphia is a city in the US. At the same time, the correlation between how these concepts are related stands at 0.42 which hints to the fact that there may be differences in implementation of the topic of study. Although the concepts used in the USC from UN and Philadelphia Smart City Road Map document seem to have a good correlation, the way they are related is the lowest in our analysis and we attribute this fact to the difference in reach of those two organizations. Further, we generated a conceptual map for each of the three documents. The generated maps varied in size and detail level. For example, the map for the USC from UN document contains 167 SUMO concepts and 967 relations among them, as shown in Table 4. At the same time, the US Smart City fact sheet and Philadelphia Smart City Roadmap semantic maps are much larger, at the same level, with 297 and 327 concepts respectively. Even reducing these maps at the Level 5 in the SUMO ontology will result in rather large semantic maps which are difficult to present here. For that reason, we show the semantic networks at the SUMO subject file level for each document. The generated networks are shown in Figures 5-7. We also applied the PathFinder networks algorithm [43] to simplify these networks. The PathFinder networks algorithm simplify the semantic network by removing the most irrelevant relationships and maintaining an acyclical graph. In these figures, the relevant connections are represented in solid line having the width proportional with its relevance while the reduced/irrelevant relationships are shown in dotted line. For example, in Figure 5 we notice that there is a relevant connection between _Housing_ and _Travel_ but this would be reduced when we applied the PathFinder Networks algorithm because each of those concepts have stronger relationships to the central node. On these figures we can also observe a different approach to categories that relate to processes, geography, and society. The importance of these categories increases with an increase in the locality of the policy. At the most global level, the UN policy only talks about these issues in general terms without too much emphasis. We attribute this to the fact that these issues need to be addressed at a local level. The USA policy emphasizes more on these categories by stressing the role of related categories such as Government and Engineering. At the most local level, the Philadelphia policy has the strongest focus on these policies and adds information on specific topics that are used in the implementation of these policies such as technology, media and transportation. It is also important to mention that, as it will be shown later in this section, the subtopics addressed at each level vary with the locality degree. For example, climate change, which is typically included in these categories, is addressed differently by the three policies. We also observe that the number of smart city concept areas vary from one document to the other with fewer concept areas addressed by higher level entities that cover more \begin{table}
\begin{tabular}{c c c} \hline \hline  & **United Nations** & **United States** & **Philadelphia** \\ \hline United Nations & & **0.40** & **0.46** \\ United States & _0.43_ & & **0.67** \\ Philadelphia & _0.32_ & _0.42_ & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation of relevance for the three documents.