## 2 Related Work  
Earlier studies of smart city initiatives focused on developing a framework to systematically understand smart city initiatives based on review of smart city literature. Smart city initiatives framework in [11] identified a set of factors including technology, management and organization, policy, governance, people and communities, economy, built infrastructure, and natural environment as the fundamental elements of smart city initiatives. In a follow up empirical study [12], this smart city initiatives framework is used to assess smart city initiatives of different cities including Philadelphia, Seattle, Quebec City, and Mexico City. Recent study on smart city initiatives [13] focus on country specific factors and study how characteristics unique to a country may impact the development of smart city initiatives. A comparative analysis between US and China smart city initiatives revealed the impact of government system structure on smart city initiatives. A Smart City Initiative Design (SCID) Framework is constructed in [14] as a framework to support design of smart city initiatives. SCID framework provided common objectives for smart city initiatives, strategies and enabling factors for successful initiative design, and core challenges to be addressed. European Union report [15] analyzed smart city initiatives across European Union to understand the potential impact of the initiatives on European Union 2020 objectives as well as alignment of the initiatives to European Union 2020 objectives. Study emphasized the need for designing smart city initiatives that are based on specific, measurable, achievable, realistic, and time dependent (SMART) objectives and that are aligned with city development plans and European Union 2020 objectives. Another study of smart city initiative [1] constructed a regression analysis to understand the relationship between smart city contextual variables and coverage index which considers number of application domains a city has launched initiatives. Results of the study revealed the importance of understanding the local context as there is no global definition of smart city. Other studies also emphasized the need to understand local contextual factors and integrate disparate and ambiguous information [16; 17]. Smart city initiatives are not designed in isolation from the states, regions, governments in which they reside. Higher level administrative levels may impose incentives or constraints on the local cities, so cities need to understand how smart city initiatives align with higher level administrations. Multi-level analysis of initiatives necessitates evaluation of contextual characteristics of knowledge representation [18] at different granularity levels. This requires establishing consensus among decision makers which in a community is knowledge based [19]. Formalization [20] and abstraction [21] of knowledge are some of the key methods for establishing consensus in a community. Ontologies can be used for formalization and abstraction of text [22] to represent, exchange, and interpret knowledge as well as compare knowledge structures from different data sources and formats [6]. There are several classification schemes for linguistic content including [PERSON]'s classification of English verbs [23], WordNet semantic network classification of nouns, verbs, adjectives, and adverbs [24]. There are several formal ontologies including DOLCE [25], BFO [26], and SUMO [27]. Among these formal ontologies, The Suggested Upper Merged Ontology (SUMO) provides greater variety and completeness of definitions and has been mapped to WordNet which provides the capability to convert English words to formal terms. Therefore, SUMO ontology can be utilized for reducing ambiguity in text documents using formal terms [28]. Knowledge extraction in smart city domain is challenging due to variations among semantics of data. Several studies tackled knowledge extraction of text from smart city documents to design smart city applications and services.