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 organ ized 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 an alysis and decision - making.

BFO 2020 does not support functions, dispositions, and roles of generically dependent continuants (like software or datasets). In this paper, we argue that this is a severe limitation, which prevents, for example, the adequate representation of the functions of computer models or the various roles of datasets during the execution of these models. We discuss the aspects of BFO 2020 that prevent the representation of realizable entities of generically dependent continuants. Two approaches to address the issue are presented: (a) the use of defined classes and (b) a proposal of changes that allow BFO to support functions, dispositions, and roles of generically dependent continuants. The latter also addresses limitations of BFO 2020 concerning the roles and dispositions of immaterial entities, particularly boundaries and sites.

The paper explores the nature of risk, providing a characterization using the categories of the Basic Formal Ontology (BFO). It argues that the category Risk is a subclass of BFO:Role, contrasting it with a similar view classifying Risk as a subclass of BFO:Disposition. This modeling choice is applied on one example of risk, which represents objects, processes (both physical and mental) and their interrelations, then generalizing from the instances in the example to obtain an overall analysis of risk, making explicit what are the sufficient conditions for being a risk. Plausible necessary conditions are also mentioned for future work. Index Terms: ontology, risk, BFO, role, disposition

Ontological representations of qualities, dispositions, and roles have been refined over the past decade, clarifying subtle distinctions in life science research. After articulating a widely-used characterization of these entities within the context of Basic Formal Ontology (BFO), we identify gaps in this treatment and motivate the need for supplementing the BFO characterization. By way of supplement, we propose definitions for grounding relations holding between qualities and dispositions, and dispositions and roles, illustrating our proposal by representing subtle aspects of host-pathogen interactions.

In our daily lives, as in science and in all other domains, we encounter huge numbers of dispositions (tendencies, potentials, powers) which are realized in processes such as sneezing, sweating, shedding dandruff, and on and on. Among this plethora of what we can think of as mere dispositions is a subset of dispositions in whose realizations we have an interest a car responding well when driven on ice, a rabbits lungs responding well when it is chased by a wolf, and so on. We call the latter capabilities and we attempt to provide a robust ontological account of what capabilities are that is of sufficient generality to serve a variety of purposes, for example by providing a useful extension to ontology-based research in areas where capabilities data are currently being collected in siloed fashion.