利用时空叙事预测基于位置的事件

R. Santos, Sumit Shah, F. Chen, Arnold P. Boedihardjo, Chang-Tien Lu, Naren Ramakrishnan
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引用次数: 9

摘要

讲故事这种通过关系连接实体的行为,为探索性分析提供了一个直观的平台。本文将讲故事和空间逻辑推理(SLI)结合起来,生成实体之间的交互规则,并衡量它们对现实世界事件的预测程度。该算法首先将事件发生的概率及其空间距离作为输入。它计算它们的软真理,即它们确实被确定地观察到的信念。随后,该算法采用一种放松形式的逻辑合取和析取来计算每条规则的满足距离。最小距离的规则代表了最好的预测。对阿富汗社会动荡进行的大量实验表明,讲故事和特殊语言障碍比普通概率方法的准确率高30%,在召回率方面高13%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting location-based events with spatio-temporal storytelling
Storytelling, the act of connecting entities through relationships, provides an intuitive platform for exploratory analysis. This paper combines storytelling and Spatio-logical Inference (SLI) to generate rules of interaction among entities and measure how well they forecast a real-world event. The proposed algorithm first takes as input the probability of prior occurrences of events along with their spatial distances. It calculates their soft truths, i.e., the belief they have indeed been observed with certainty. Subsequently, the algorithm applies a relaxed form of logical conjunction and disjunction to compute a distance to satisfaction for each rule. The rules of lowest distances represent the best forecasts. Extensive experiments with social unrest in Afghanistan show that storytelling and SLI can outperform common probabilistic approaches by as much as 30% in terms of precision and 13% in terms of recall.
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