论文标题
移动链接预测：自动创建和众包知识图验证
Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs
论文作者
论文摘要
为网络物理社会系统（CPS）建立可信赖的知识图是一个挑战。特别是，依靠人类专家的当前方法的可扩展性有限，而自动化方法通常不对导致知识质量的知识图负责。本文介绍了一个新颖的普遍知识图构建器，将自动化，专家和众包公民的知识汇集在一起。知识图通过使用遗传编程的自动链接预测来增长，这些遗传编程被人类验证，以提高透明度和校准精度。知识图构建器是为普遍存在的设备（例如智能手机）设计的，并通过本地化所有计算来保留隐私。知识图建造器的准确性，实用性和可用性在现实世界中的社交实验中进行了评估，该实验涉及智能手机实施和智能城市应用程序方案。所提出的知识图构建方法在准确性方面优于基线方法，同时证明了其对智能手机的有效计算以及在高相互作用吞吐量方面的普遍性人类监督过程的可行性。这些发现有助于众包和运营智能城市网络物理社会系统的普遍推理系统的新机会。
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.
