With billions of facts referencing connected entities inside a graph database, this information source can quickly become the foundation for knowledge discovery and knowledge management. Today, organizations can structure their unstructured data, add additional free facts from Linked Open Data sets, combine all of this with a controlled vocabulary, thesauri, taxonomies or ontologies which, to one degree or another, are used to classify the stored entities and depict relationships. Real knowledge is then surfaced from the results of queries, visual analysis of graphs or both. Everything is indexed inside the triplestore.
Graph databases (and specialized versions called native RDF triplestores that embody reasoning power) show great promise in knowledge discovery, data management and analysis. They reveal simplicity within complexity. When combined with text mining, their value grows tremendously. As the database ecosystem continues to grow, as more and more connections are formed, as unstructured data multiplies with fury, the need to analyze text and structure results inside graph databases is becoming an essential part of the database ecosystem. Today, these combined technologies are available and not just reserved for the big search engines providers. It may be time for you to consider how to better store, manage, query and analyze your own data. Graph databases are the answer.