Autonomous place naming system using opportunistic crowdsensing and knowledge from crowdsourcing

A user's location information is commonly used in diverse mobile services, yet providing the actual name or semantic meaning of a place is challenging. Previous works required manual user interventions for place naming, such as searching by additional keywords and/or selecting place in a list. We believe that applying mobile sensing techniques to this problem can greatly reduce user intervention. In this paper, we present an autonomous place naming system using opportunistic crowdsensing and knowledge from crowd sourcing. Our goal is to provide a place name from a person's perspective: that is, functional name (e.g., food place, shopping place), business name (e.g., Starbucks, Apple Store), or personal name (e.g., my home, my workplace). The main idea is to bridge the gap between crowdsensing data from smartphone users and location information in social network services. The proposed system automatically extracts a wide range of semantic features about the places from both crowd-sensing data and social networks to model a place name. We then infer the place name by linking the crowdsensing data with knowledge in social networks. Extensive evaluations with real deployments show that the proposed system outperforms the related approaches and greatly reduces user intervention for place naming.