An Extended Maritime Domain Awareness Probabilistic Ontology Derived from Human-aided Multi-Entity Bayesian Networks Learning

Ontologies have been commonly associated with representing a domain using deterministic information. Probabilistic Ontologies extend this capability by incorporating formal probabilistic semantics. PR-OWL is a language that extends OWL with semantics based on Multi-Entity Bayesian Networks (MEBN), a Bayesian probabilistic logic. Developing probabilistic ontologies can be greatly facilitated by the use of a modeling framework such as the Uncertainty Modeling Process for Semantic Technology (UMP-ST). An example of using UMPST was the development of a probabilistic ontology to support PROGNOS (PRobabilistic OntoloGies for Net-Centric Operational Systems), a system that supports Maritime Domain Awareness (MDA). The PROGNOS probabilistic ontology provides semantically aware uncertainty management to support fusion of heterogeneous input and probabilistic assessment of situations to improve MDA. However, manually developing and maintaining a probabilistic ontology is a labor-intensive and insufficiently agile process. Greater automation through a combination of reference models and machine learning methods may enhance agility in probabilistic situation awareness (PSAW) systems. For this reason, a process for Human-aided MEBN Learning in PSAW (HMLP) was suggested. In previous work, we used UMP-ST to develop the PROGNOS probabilistic ontology. This paper presents an extended PROGNOS probabilistic ontology developed using HMLP. The contribution of this research is to introduce the extended PROGNOS probabilistic ontology and present a comparison between two processes (UMP-