用知识图谱预测渔船未来轨迹，融合时空语义与概率模型。
Integrating Activity Predictions in Knowledge Graphs
- 基于本体构建渔船活动的时空知识图谱
- 通过马尔可夫链模型实现未来状态预测
- 适合需动态追踪与决策支持的场景
我们主张，结构化的本体知识图谱在预测未来事件中具有关键作用。借助基本形式本体（BFO）和通用核心本体（CCO）提供的语义框架，我们展示如何将渔船移动等数据组织并检索于知识图谱中。查询结果用于构建马尔可夫链模型，基于历史记录预测未来状态。为完善结构语义，我们引入术语“时空实例”。同时，我们批判当前将概率视为面向未来的主流本体模型，提出至少部分概率应针对实际过程特征，更贴合现实动态。最后，展示基于马尔可夫链的概率计算可无缝回填至知识图谱，支持进一步分析与决策。
原文摘要 · Abstract (English)
We argue that ontology-structured knowledge graphs can play a crucial role in generating predictions about future events. By leveraging the semantic framework provided by Basic Formal Ontology (BFO) and Common Core Ontologies (CCO), we demonstrate how data such as the movements of a fishing vessel can be organized in and retrieved from a knowledge graph. These query results are then used to create Markov chain models, allowing us to predict future states based on the vessel's history. To fully support this process, we introduce the term `spatiotemporal instant' to complete the necessary structural semantics. Additionally, we critique the prevailing ontological model of probability, according to which probabilities are about the future. We propose an alternative view, where at least some probabilities are treated as being about actual process profiles, which better captures the dynamics of real-world phenomena. Finally, we demonstrate how our Markov chain-based probability calculations can be seamlessly integrated back into the knowledge graph, enabling further analysis and decision-making.
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