Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain

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.

Keywords

• multi-level initiatives
• policy context
• knowledge elicitation
• natural language processing
• semantic fusion
• deep learning