DISTRIBUTED SPACECRAFT MISSIONS (DSMS) ARE GAINING INTEREST TO AUGMENT SCIENTIFIC OR OPERATIONAL PURPOSES UNDER CONSTRAINED BUDGET REQUIREMENTS. DESIGN OF DSMS DURING PRE-PHASE A ACTIVITIES MUST EXPLORE A COMPLEX COMBINATORICALLY-LARGE TRADE SPACE WHICH LIMITS OPTIMIZATION METHODS. THIS PROJECT PROPOSES A KNOWLEDGE BASE (KB) TO SUPPORT DESIGN OF DSMS USING THE TRADESPACE ANALYSIS TOOL FOR CONSTELLATIONS (TAT-C) AND MACHINE LEARNING (ML) TECHNIQUES. THE KB PROVIDES A HUMAN- AND MACHINE-READABLE STORE OF INFORMATION WITH ASSOCIATED SEMANTIC METADATA AND PROVIDES AN APPLICATION PROGRAMMING INTERFACE TO ACCESS THIS DATA ACROSS MULTIPLE MODELS. THE PROPOSED WORK BUILDS ON EXISTING PROTOTYPES DEVELOPED WITH TAT-C TO INCORPORATE NEW SEMANTIC WEB TECHNOLOGIES TO FACILITATE ORGANIZATION AND QUERYING OF COMPLEX DATA PRODUCTS. THIS WORK ALIGNS WITH RECENT INTEREST IN MODEL-BASED SYSTEMS ENGINEERING (MBSE) TO SEAMLESSLY SHARE AND ACCUMULATE DATA THROUGHOUT A PROJECT LIFECYCLE.