Knowledge graphs are quickly becoming a commonplace tool for storing relationships between entities from which higher-level reasoning can be conducted. Knowledge graphs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the knowledge graph and the storage and analysis of query results are straightforward, however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a knowledge graph with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank, and explore subgraphs of a complex knowledge graph represents a major challenge to any effort designed to exploit the use of knowledge graphs for applications in biomedical research and other domains. We present ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways) as an abstraction layer and user interface to more easily query knowledge graphs and store, rank, and explore query results.