Integrating Activity Predictions in Knowledge Graphs
This work addresses the challenge of dynamic event prediction for domains like maritime monitoring, but it is incremental as it builds on existing ontological and modeling techniques.
The paper tackles the problem of predicting future events by integrating activity predictions into knowledge graphs, using ontology-structured data like fishing vessel movements to create Markov chain models for state predictions, and demonstrates seamless integration back into the knowledge graph for analysis.
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.