Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs

TL;DR

This paper presents a novel, privacy-preserving, smartphone-based knowledge graph builder that combines automated link prediction with human validation, enhancing accuracy and scalability for cyber-physical social systems in Smart Cities.

Contribution

It introduces a pervasive knowledge graph construction method integrating automation and crowd-sourced validation, optimized for smartphones and real-world social applications.

Findings

Outperforms baseline in accuracy

Efficient calculations on smartphones

High interaction throughput in human supervision

Abstract

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.