Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs
Mark Christopher Ballandies, Evangelos Pournaras

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…
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