Accurate situational assessment is key to any decision maker and especially crucial in military command and control, air traffic control, and complex system decision making. Endsley described three dependent levels of situational awareness, (1) perception, (2) understanding, and (3) projection. This research was focused on Endsley's second-level situational awareness (understanding) as it applies to service-oriented information technology environments in the context of the Semantic Web. Specifically, this research addressed the problem of developing accurate situational assessments related to the status or health of information technology (IT) services, especially composite, dynamic IT services, when some of Endsley's first level (perceived) information was inaccurate or incomplete. Research had not adequately addressed the problem of how to work with inaccuracy and situational awareness information in order to produce accurate situational assessments for Semantic Web services. This problem becomes especially important as the current Web moves towards a Semantic Web where information technology is expected to be represented and processed by machines. Costa's probabilistic Web ontology language (PR-OWL), as extended by Carvalho (PR-OWL2), is a framework for storage of and reasoning with uncertainty information as part of the Semantic Web. This study used Costa's PR-OWL framework, as extended by Carvalho, to build an ontology that supports reasoning with service-oriented information in the context of the Semantic Web and then assessed the effectiveness of the developed ontology through the use of competency questions, as described by Gruninger and Fox and verified through the use of an automated reasoner. This research resulted in a Web Ontology Language for Services (OWL-S), PR-OWL2 based ontology, and its associated Multi-Entity Bayesian Network which are flexible and highly effective in calculating situational assessments through the propagation of posterior probabilities using Bayesian logic. Specifically, this research (1) identifies sufficient information required for effective situational awareness reasoning, (2) specifies the predicates and semantics necessary to represent service components and dependencies, (3) applies Multi-Entity Bayesian Network to reason with situational awareness information, (4) ensures the correctness and consistency of the situational awareness ontology, and (5) accurately estimates posterior probabilities consistent with situational awareness information.