I have been using GraphDB for several years now and in almost all the positions I have worked for. In my opinion, it is by far the best tool out there for managing RDF graph databases. I love the simplicity of the interface, the practicality and aesthetic superiority of the visualizations and the overal "smoothness" that comes with using it. I cannot stress enough how great the technical support has always been at responding blazingly fast and always providing solutions to various issues one may come accross, even if they are GraphDB Free users.

- Compliance to all the W3C standards, including SHACL. - User-friendly UI. - Aesthetically superior visualizations. - Great documentation: As detailed as it needs to be, but not more than that. - Great technical support (even for the GraphDB Free version users) that responds almost immediately.

I honestly cannot think of anything negative to say about the tool and the customer support. Even though the company behind the tool (Ontotext) is a big global enterprise, every interaction I have with them feels like they genuinely care about whatever issues I am having with GraphDB and honestly want to help out in resolving it.

There are other triple stores with different feature sets, but it don't think there's any triplestore that is better than GraphDB.

It's very quick and easy to deploy GraphDB, ingest some RDF data, build queries in a IDE-like environment, and visualize relationships. A semantic similarity search tool is provided.

The OntoRefine tool is great for converting tabular data files into semantic triples, but there's no support for reading from relational databases. There are nice free text indexing & search tools, but no natural language parser for discovering entities and relationships. There are several pre-configured reasoning levels plus support for writing one's own rules, but no support for SWRL. Like most triplesotres, OWL2 reasoning over complex axioms and millions of data triples isn't fun/fast/realistic? (I say that based on a single node, two threads, and 256 GB RAM.)

We are currently using graphdb in a PoC as semantic web stack compliant database for data integration in a laboratory environment.

- ease of use (compared to other semantic web stack solutions) - degree of inferencing implementation - solution for transforming relational data into RDF with OpenRefine integration - query performance (for SELECT, a evaluation for INSERT queries could not be given due to use of free version) - good support even at free version

- versioning of data (see changes over time) - better controllability of role and rights (give rights for specific graphs in repository) - no IdP based authentiaction like OpenID Connect (or something based on oAuth2 or at least SAML) - easy to use integration for object storage (like AWS S3) - documentation could be more detailed in some places

- Easy to use - Fast to deploy - Performant for reasonement - Support most of important things about W3C ontologies

- Some features are restricted for enterprise (example for some access control features) - Expensive if you work alone and need a license for this kind of infrastructure

We use GraphDB together with PoolParty as part of the Semantic Integrator solution. We use GraphDB as our test triple store. We use GraphDB to publish our "small" linked open data sets.

I can be very short about my (our) experiences so far with GraphDB. GraphDB is a clear winner for our usecases now. The learning curve is not steep, almost self-explanatory. It’s fast and it fits our needs; for now. We loved the graphics of GraphDB.

OntoRefine. It looks fine but we missed some modeling features. We switched back to OpenRefine.