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.

We've had a really good time working with it. Once we have a good model definition (which applies for any graph engine) it simply works and works really well.

Over the last years, GraphDB has improved a lot, speaking about performance and inference. Graph DBs have always had the problem of being too slow for solving queries, we always had to structure them in a way that it was optimal for the engine to solve them. Graph DB has been always the most performant one. Another feature that I loved from it is that, in order to install it, you simply copy a single jar file and you're almost ready to go. The user interface helps a lot. And its compliance with several standards for the interchange formats makes the way pretty straight forward no matter what tool was used to generate an interchange file.

The con I find with the product is about updates. When a new version comes up (which sometimes I don't know about the fact that there's a new release), I need to manually download it and deploy it. Also it would be desirable if we had access to connectors for different platforms. We work a lot with node.js and there's almost no libraries to use it with graphDB that are backed by ontotext.

The use of graphDB solves many speed issues as it is very fast. The webservice api allows manipulation of the repositories (create, delete, import). The only problem is that after some updates, the speed of the endpoint queries dorps very much. An example for this if the migration from v8.11.1 to v9.0. I am using the free version but in a single thread and they mention that the only limitation of the free version is the limitation of simultaneous queries to only two. I had the same problems when i tried the enterprise edition for a trial period and never managed to reach the speed of the free version with any change I tried in the setup or options.

Sparql support. Ease of setup. Webservice api. Functionality of free version.

Speed changes after some updates. Removal from GCC

I am using GraphDB for visualizing data which I created for our two EU project I am working on. One is a Healthcare project with NHS Scotland and the other is a transportation project. I also advise​ the ​student of my class to use this product which has a ​grate feature.

-Seamless​ navigation from one tab to another tab -Similarity​ matrix -Index -Visualization -Google map view -documentation

-the way it represents​ class hierarchy -NA

Simplicty i liked unlike SQL Databases where relationship becomes so complex and then you Normalize. Being Simple, we can easily handle objects in very easy way.

The UI should be better like MS Mangement Studio. Also the server services etc should be part of the software rather starting it manually.

I learned a tremendous amount about data structures / formats, and through iterative work, I learned more about how to structure an ontology. (Note: Accounting is not my industry, even though this survey suggests I'm in accounting)

1) The ease of import/export. 2) The visualizations, overall platform direction, and ease of updates. 3) Class hierarchies and visual graph. 4) SPARQL Query & Update (Editor and Results) feature.

1) Tutorials. These could be greatly improved. 2) Import fails are difficult to clear? 3) Interdisciplinary users have difficulty interpreting impact of "repository setup" selections. 4) Broad description specific to each feature / utility and how it can be utilized. For example, this is a Graph Database tool, but can any aspect be specifically used for ontologies. If yes, which one and to what specificity?

NA

Graph database seem to be tailor-mode for networking applications. 1. The prototypical example is a social network,where nodes represent users who have various kinds of relationships to each other.Modeling this kind of data using any of the other styles is offten a tough fit,but a graph database would accept it with relish. 2.They are also perfect matches for a object-oriented system. 3.The Model in a graph database is easier to change than in a relation database. if the type of relationship in an RDBMS changes e.g from 1:N to M:N I need to use DDL to change the model, which may require downtime. I don't have this problem in a graph database where changing the relationships/edges between vertices is much easier.

1. Graph Database not much connectors. 2. Because of the high degree of interconnectedness between nodes,graph database are generall not suitable for network patitioning. 3. Graph databases don't scale out well. 4. Graph database are poor at aggregating data,Don't use it for Business Intelligence. 5. It is difficult to implement versioning/auditing of data in a graph database. while not impossible it is rather awkward though.

Well, GraphDB is definitely one of the "triplestores of choice". It's suitable both for experiments and for production.

Good performance, a lot of features, great UI and documentation, ease of administration and use.

OWL 2 DL reasoning is not supported. The LPG model (i.e. RDF*/SPARQL* or Gremlin) is not supported.

Easy setup and install of local graphDB by loading .ttl files. Hdt support would be appreiciated

The interface is intuitive and it is easy to use tables returned form graph queries to further browse and explore

Visualization of the graph does not work, at least not with the settings I tried (no inference) If graph visualization would be perfect (e.g. see the RDF2Graph) and vizualization could be used to add paterns to the query, I would definetely give GraphDB 5 stars, it would be awesome

Overall it is a very experience and since I have started using GraphDB, I have not switched to any other semantic platform.

The software is easy to use. The User interface is quite nice, and appealing. The blogs are well written and very informative, and is a good means to keep customers updated with the latest developments in the area of semantics.

Get some more documentation on the use of Sparql for example - how to formulate complex joins.

I like its toolkit which provide almost all the feature that you may need while solving any problem. I have used GraphDB for my University assignment , it didn't take much time for me as it is easy to understand and work like plug and play.

I like graphDB and i didn't find disadvantage of using it but of course it take lots of time to download and bit slow (sometime) when i used 4 GB ram with intel i3 processor.

-The Ease of Use of the Rest service which allows you to import vast data at once. -The speed of the server even in the free edition with the limited number of the concurrent queries.

If I had to pick something, it would be the heavy workbench that uses too much memory. It is expected in general but still it is annoying to have a non responsive browser after a few hours.

Triple store database for my semantic web and linked data applications.

Good reasoning support. Fast query response. Good documentation. Good user interface. Excellent API. Good visualisation tools. East to setup, load RDF and query.

Can get a bit buggy at times. Occasionally crashes and can slow down the computer when using the standalone application.

Ease of use.

Very easy to use when trying to parse through qualitative data for my dissertation. Needed to code data from multiple reflections and interviews.

was very easy to use.. took a little while to get up to speed, but once I did, I found it very intuitive and easy to use.

OntoRefine is a great way of moving tabular data in triples. The APIs are very well designed, Customer service and technical support is excellent

Explore function could be better developed. Other tool provide the ability to search by triples (not through SPARQL)

Easy set-up, works out of the box.

Works out of the box, easy configuration. I tried using Jena/Fuseki before, but the configuration is much more laborious and it began unusable after a couple minutes because of memory leaks. No such problem with GraphDB

The documentation is not always so good for specific problems. For instance, I tried doing requests to my OntoDB server from outside the web interface with secure accounts activated, but I never found how to request tokens from the server.

Ontorefine has some very powerfull features for cleaning data, right where you need it. Visual exploration of graphs is really good and compered to other tools on the market, rund smoothly without draing battery,

Autocompletion of sparql queries or some other way of helping developers creating queries. Maybe some template queries for the most basic of queries would be a great help.

Run smoothly and once you know how to use it you can perform any tasks that involves search in nodes or relations. Good benchmark when executing tasks using Dijkstra's and Prim.

Not for beginners, takes a time to learn how to use the sintax for graph dbs but it is the technology language and not the product.

with the right knowledge you will be equipped to take advantage of the performance of GraphDB, it is very quick.

there is a learning curve so just get ready and have some patience you will get through it and then you will enjoy it.

Easy to use, user-friendly interface, it makes my life easier!

none

Speed up performance

I Like this product because is easy to start, it has a REST api and UI simple to use. The fact that the free version is more or less the same in functionality than the commercial version is also appreciable.

In my use case there is not a thing that i don't like, but for further use, I read that transaction is not well implemented, and we hope is possible to configure the url-path of api and gui in a separate way