Ontology-based urban data exploration
Booma Sowkarthiga Balasubramani, Vivek R. Shivaprabhu, Smitha Krishnamurthy, Isabel F. Cruz, Tanu Malik · 2016
Cities are actively creating open data portals to enable predictive analytics of urban data. However, the large number of observable patterns that can be extracted as rules by techniques such as Association Rule Mining (ARM) makes the task of sifting through patterns a tedious and time-consuming task. In this paper, we explore the use of domain ontologies to: (i) filter and prune rules that are variations of a more general concept in the ontology, and (ii) replace groups of rules by a single general rule with the intent of downsizing the number of initial rules while preserving the semantics. We show how the combination of several methods reduces significantly the number of rules thus effectively allowing city administrators to use open data to generate patterns, use them for decision making, and better direct limited government resources.