Very easy to use. Great support.

Ease of use.

Pros

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

Cons

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.

Nearly instantaneous, rich-featured semantic repository

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

Pros

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.

Cons

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.)

review of graphdb in the production planning

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

Pros

- 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

Cons

- 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

GraphDB

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.

Pros

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.

Cons

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

One of the best for graph databases

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.

Pros

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

Cons

Speed changes after some updates. Removal from GCC

Perfect tool for your knowledge graph

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.

Pros

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

Cons

-the way it represents​ class hierarchy -NA

Amazing i will say. Easy to use and interpret the requirements to real.

Pros

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.

Cons

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

This is an excellent platform to get started in graph databases and knowledge representation.

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)

Pros

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.

Cons

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?

The best Ontological Database Engine

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.

Pros

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.

Cons

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.

Perhaps the best choice for mid-size company

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

Pros

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

Cons

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

GraphDB Enterprise is a high-performance semantic repository created by Ontotext. It is implemented.

NA

Pros

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.

Cons

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.

Review of the free version, its great! Easy and simple to install and use

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

Pros

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

Cons

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

GraphDB review?

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

Pros

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.

Cons

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

Memory Efficient and easy to understand

Pros

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.

Cons

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.

Great Graph Database Implementation with very good Rest service.

Pros

-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.

Cons

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.

Excellent triple store. I use it all the time. I've been using it for several years now.

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

Pros

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

Cons

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