Cost overruns on complex system-of-systems development programs frequently trace back to problems with requirements. For increasingly complex systems, a key capability is the identification and management of requirements early in a system’s life cycle, when errors are cheapest and easiest to correct. Significant work has been done to apply natural language processing (NLP) to the domain of requirements engineering. Recently, requirements engineering tools have been developed that use NLP to leverage both domain ontologies and requirement templates, which define acceptable sentence structures for requirements. Domain ontologies provide terminology consistency, and enable rule-checking during the testing of requirements. This thesis introduces FLOOR, a new software tool for requirements engineering that leverages NLP. FLOOR not only integrates domain ontologies and requirement templates, but also supports importing multiple external domain ontologies.