Was ist GraphDB?
Graph DB ist eine semantische Graph-Datenbank, mit der Organisationen Inhalte in Form semantisch angereicherter intelligenter Daten speichern, organisieren und verwalten können. Graph DB umfasst Datenintegration und -verknüpfung, Einhaltung der W3C-Standards, ausdrucksstarkes, umfangreiches und flexibles Datenmodell, einen einzigen zusammenhängenden Informationsbereich, der aus strukturierten Daten und Textdokumenten besteht, Argumentation, verknüpfte Open-Data-Kompatibilität und Datenherkunft.
Wer verwendet GraphDB?
GraphDB ist die bevorzugte semantische Graph-Datenbank für Medienunternehmen, Verlage, Museen, Wissenschaft, Forschung im Bereich Gesundheitswesen und Biotechnologie, Versicherungen, Geschäfts- und Risikoanalysen, Marketingdienste.
Wo kann GraphDB bereitgestellt werden?
Cloud, SaaS, webbasiert, Mac (Desktop), Windows (Desktop), Linux (Desktop), Windows (On-Premise), Linux (On-Premise)
Über den Anbieter
- Ontotext
- Ansässig in Sofia, Bulgarien
- 2008 gegründet
- Telefon-Support
- Chat
Erhältlich in diesen Ländern
Bulgarien
Sprachen
Nicht vom Anbieter bereitgestellt
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Über den Anbieter
- Ontotext
- Ansässig in Sofia, Bulgarien
- 2008 gegründet
- Telefon-Support
- Chat
Erhältlich in diesen Ländern
Bulgarien
Sprachen
Nicht vom Anbieter bereitgestellt
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Bewertungen über GraphDB
Peter C.
Very easy to use. Great support.
Kommentare: Ease of use.
Vorteile:
Very easy to use when trying to parse through qualitative data for my dissertation. Needed to code data from multiple reflections and interviews.
Nachteile:
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.
Mark M.
In Betracht gezogene Alternativen:
Nearly instantaneous, rich-featured semantic repository
Kommentare: There are other triple stores with different feature sets, but it don't think there's any triplestore that is better than GraphDB.
Vorteile:
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.
Nachteile:
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.)
Antwort von Ontotext
vor 3 Jahren
Thank you for your feedback, dear Mark! We will address all the recommendations you have left to the production team. Be well
Alexander R.
In Betracht gezogene Alternativen:
review of graphdb in the production planning
Kommentare: We are currently using graphdb in a PoC as semantic web stack compliant database for data integration in a laboratory environment.
Vorteile:
- 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
Nachteile:
- 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
Antwort von Ontotext
vor 3 Jahren
Thank you for your feedback!
Joop V.
In Betracht gezogene Alternativen:
GraphDB
Kommentare:
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.
Vorteile:
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.
Nachteile:
OntoRefine. It looks fine but we missed some modeling features. We switched back to OpenRefine.
Antwort von Ontotext
vor 3 Jahren
Thank you for your feedback, Joop. Be well!
Adonay andres A.
The best Ontological Database Engine
Kommentare: 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.
Vorteile:
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.
Nachteile:
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.
Antwort von Ontotext
vor 3 Jahren
Dear Adonay, thank you for your great feedback! It is always great to see some extended prons and cons of the product you create! Regarding the cons: If you are part of the GraphDB Update announcements list you should receive a note about every update of GraphDB, so you won't be missing anything new. About the connectors: Latest releases of GraphDB support connectors and plugins to MongoDB Lucene, SOLR, Elasticsearch. If you need connectors to other popular services, you should make a request to the GraphDB Production team. Best Regards
