###### Abstract  
Decision makers and policy analysts at different administrative levels often lack a holistic view of the problem as there are semantic variations in policy documents due to domain-specific content. For example, smart city initiatives are derived from national and international initiatives which may influence the incentives for local participants, but local initiatives reflect the local contextual elements of the city. Balanced assessment of smart city initiatives should include a systemic evaluation of the initiatives at multiple levels including the city, the country in which the city resides as well as at international level. In this paper, a knowledge elicitation methodology is presented for multi-granularity evaluation of policies and initiatives. The methodology is demonstrated on the evaluation of smart city initiatives generated at different administrative levels. Semantic networks are constructed using formal ontologies and deep learning methods for automatic semantic evaluation of initiatives to abstract knowledge found in text. Three smart city initiatives published by different administrative levels including international, national, and city level are evaluated in terms of relevance, coherence, and alignment of multi-level smart city initiatives. Experiments and analysis ultimately provide a holistic view of the problem which is necessary for decision makers and policy analysts of smart cities. m _Applied_  
10037. [[https://doi.org/10.3390/app112110037](https://doi.org/10.3390/app112110037)]([https://doi.org/10.3390/app112110037](https://doi.org/10.3390/app112110037))  
* [2021] **C  
that indirectly benefit from the initiative, the city as a whole, other cities, the country as well as at international level. Thus, complexity stems from the multi-stakeholder nature of cities where multi-level of agencies, perspectives, private and public sectors are involved. This multiplicity of perspectives is evident in the wide range of smart city initiatives across the world which cover a wide range of contexts and situations. For example, smart city initiatives in Barcelona aim to transform the city using advanced digital technologies with a focus on creating a sustainable city and innovative commerce ecosystem [2]. Initiatives in Amsterdam focus on addressing climate change using smart infrastructure technologies. Other smart city initiatives prioritize design of technologies to improve participation of its citizens [3]. Assessment of initiatives is challenging since stakeholders have varying and, in some cases, conflicting objectives. Besides some of the initiatives are derived from national and international levels which may influence the incentives for local participants but do not reflect the local contextual elements of the initiative. Both local and central governments lack a consistent and complete plan for balanced assessment of initiatives and policies for smart cities. Decision makers need methods and tools to assess the broad impact of smart city initiatives and policies. They also need to understand the differences and synergies among multi-domain, multi-level contextual perspectives among initiatives so that gaps and strengths of the initiatives can be identified, and initiatives can be aligned with national and international policy objectives. The open and complex nature of the problem necessitates knowledge elicitation methodologies that promote holistic view for evaluating multi-level initiatives and policies [4; 5]. Formalized semantic networks provides means to structurally represent text [6]. These formalized semantic networks can form a holistic view of knowledge which decision makers and analysts at different levels of administrations can utilize to analyze information. However, generating a formalized semantic network is challenging due to the complexity, ambiguity, and variability of text. Ontologies provide conceptual models of a domain and can be used to reduce complexity and ambiguity in text [6].