## 4 Experiments
### Analysis and Discussion  
To evaluate the effectiveness of our methodology, we analyzed each of the documents and manually extracted a list of smart city topics in those documents. A complete list of these goals is shown in Table 6. For example, \"climate change\" is explicitly mentioned in the USC from United Nations and US Smart City fact sheet documents but not in the Philadelphia Smart City Roadmap document which mentions \"climate resiliency\" only. For each of the documents we ranked each topic by computing the similarity of each subject and object in the generated triplets to the description of the topic. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Goal**} & \multicolumn{2}{l}{**U.N.**} & \multicolumn{2}{l}{**U.S.A.**} & \multicolumn{2}{l}{**PHL.**} \\  & **Act** & **Pred** & **Act** & **Pred** & **Act** & **Pred** \\ \hline City Services & x & 3 & x & 1 & x & 1 \\ Clean Energy & x & 7 & & 11 & & 11 \\ Climate Change & x & 4 & x & 6 & & 4 \\ Demographic Changes & x & 2 & & 9 & & 9 \\ Economic Growth & & 10 & x & 3 & & 8 \\ Energy Consumption & & 9 & & 4 & x & 3 \\ Public-Private Partnership & x & 8 & & 7 & x & 5 \\ Public Safety & & 11 & x & 10 & & 10 \\ Quality of Life & x & 5 & x & 2 & x & 2 \\ Traffic management & & 6 & x & 5 & & 6 \\ Urban Migration & x & 1 & & 8 & & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Smart City objectives for each document analyzed and the predicted rank of the topic in the document. Figure 8: Quadrant of relevance variation for average ranking of each smart city area. As seen in Table 6 the topic _City Services_ is ranked consistently high as a topic for all the documents, while the _Urban Migration_ is correctly ranked high for the USC from UN document and low for the other two documents. However, _Public Safety_ is ranked consistently low although it should be ranked higher for the US Smart City fact sheet document. Overall, the average mean precision was 94% for the USC from UN document, 78% for the US Smart City fact sheet document and 91% for the Philadelphia Smart City Roadmap document. Also, the recall rate at the identified number of topic rate is 71% for the USC from UN document, 83% for the US Smart City fact sheet document, and 75% for the Philadelphia Smart City Roadmap document. For example, the UN document identifies seven relevant smart city areas. If we retrieved the top seven topic as ranked by our algorithm, we would recall 71% of the relevant topics. These results reveal that our methodology is promising in terms of automatically retrieving relevant smart city areas for the US and Philadelphia documents. Further refinement of the methodology is necessary to retrieve smart city areas more effectively for the UN smart city initiative document. To summarize, our formalized semantic networks of UN, US, and Philadelphia smart city initiative documents provide a common framework which decision makers and policy analysts can utilize to analyze initiatives generated at different administrative levels. Since ambiguity and variations among text is reduced by analyzing semantic networks at the same abstraction or granularity level, comparative analysis can be constructed to understand how the initiatives align or differ from others. Our analysis revealed several smart city areas that are shared among all three initiatives including _Computing_ and _Travel_. These smart city areas emerge as common concepts valued across international, national, and city level systems. Using the semantic networks in Figures 5-7, we also observe variations among initiatives that capture localized or system level smart city areas. For example, smart city area _Economy_ is important for US Smart City initiative, but this area does not explicitly show up in the Philadelphia smart city semantic network. If decision makers want to scale the Philadelphia smart city initiative to other US cities or across the USA, then they need to address _Economy_ area explicitly. A similar comparison can be done for high level administrators.