Integrating Activity Predictions in Knowledge Graphs

TL;DR

This paper shows how integrating semantic knowledge graphs with probabilistic models like Markov chains can improve future event prediction by leveraging ontological structures and redefining probability concepts.

Contribution

It introduces a framework combining BFO and CCO ontologies with Markov chains for event prediction and proposes a new perspective on probabilities as process profiles.

Findings

Knowledge graphs can effectively organize and retrieve spatiotemporal data.

Markov chain models can predict future states from historical data.

Probabilities are better modeled as process profiles rather than solely about the future.

Abstract

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.