Mobile link prediction: Automated creation and crowdsourced validation of knowledge graphs
Mark C. Ballandies, Evangelos Pournaras · Microprocessors and Microsystems · 2021
Building trustworthy knowledge graphs for cyber–physical social systems (CPSS) is a challenge. In particular, current approaches relying on human experts have limited scalability, while automated approaches are often not validated by users resulting in knowledge graphs of questionable quality. This paper introduces a novel pervasive knowledge graph builder for mobile devices that brings together automation, experts’ and crowdsourced citizens’ knowledge. The knowledge graph grows via automated link predictions using genetic programming that are validated by humans for improving transparency and calibrating accuracy. The knowledge graph builder is designed for pervasive devices such as smartphones and preserves privacy by localizing all computations. The accuracy, practicality, and usability of the knowledge graph builder is evaluated in a real-world social experiment that involves a smartphone implementation and a Smart City application scenario. The proposed methodology of knowledge graph building outperforms a baseline method in terms of accuracy while demonstrating its efficient calculations on smartphones and the feasibility of the pervasive human supervision process in terms of high interactions throughput. These findings promise new opportunities to crowdsource and operate pervasive reasoning systems for cyber–physical social systems in Smart Cities.