  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
accountable to users resulting in knowledge graphs of questionable quality.
This paper introduces a novel pervasive knowledge graph builder that brings
together automation, experts' and crowd-sourced 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 knowledge graph building
methodology outperforms the 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 crowd-source and
operate pervasive reasoning systems for cyber-physical social systems in Smart
Cities.
