Question with graphs is whether you want it to be a representation of a dataset, or persist the graph as the data itself. For persistence, I use Neo4j to repre

Question with graphs is whether you want it to be a representation of a dataset, or persist the graph as the data itself. For persistence, I use Neo4j to represent hundreds of dynamic graph ontologies, and I use the hosted version on graphenedb, which has worked just fine for my purposes. For some views, I just use NetworkX to generate interactive d3.js pages from data I have queried from the graph, or python/flask with py2neo to generate json for d3 visualizations. Some others use cytoscape for visualization, but I find that a bit dramatic for most purposes. Depending on how you would like to represent it, I can also recomment Webprotege and WebVOWL, since the graph you are creating is also in effect an ontology.