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Posted to user@spark.apache.org by Hajira Jabeen <ha...@gmail.com> on 2017/12/18 09:21:11 UTC
SANSA 0.3 (Scalable Semantic Analytics Stack) Released
Dear all,
The Smart Data Analytics group [1] is happy to announce SANSA 0.3 - the
third release of the Scalable Semantic Analytics Stack. SANSA employs
distributed computing via Apache Spark and Flink in order to allow scalable
machine learning, inference and querying capabilities for large knowledge
graphs.
Website: http://sansa-stack.net
GitHub: https://github.com/SANSA-Stack
Download: http://sansa-stack.net/downloads-usage/
ChangeLog: https://github.com/SANSA-Stack/SANSA-Stack/releases
You can find the FAQ and usage examples at http://sansa-stack.net/faq/.
The following features are currently supported by SANSA:
* Reading and writing RDF files in N-Triples, Turtle, RDF/XML, N-Quad format
* Reading OWL files in various standard formats
* Support for multiple data partitioning techniques
* SPARQL querying via Sparqlify (with some known limitations until the next
Spark 2.3.* release)
* SPARQL querying via conversion to Gremlin path traversals (experimental)
* RDFS, RDFS Simple, OWL-Horst (all in beta status), EL (experimental)
forward chaining inference
* Automatic inference plan creation (experimental)
* RDF graph clustering with different algorithms
* Rule mining from RDF graphs based AMIE+
* Terminological decision trees (experimental)
* Anomaly detection (beta)
* Distributed knowledge graph embedding approaches: TransE (beta), DistMult
(beta), several further algorithms planned
Deployment and getting started:
* There are template projects for SBT and Maven for Apache Spark as well as
for Apache Flink available [2] to get started.
* The SANSA jar files are in Maven Central i.e. in most IDEs you can just
search for “sansa” to include the dependencies in Maven projects.
* There is example code for various tasks available [3].
* We provide interactive notebooks for running and testing code [4] via
Docker.
We want to thank everyone who helped to create this release, in particular
the projects Big Data Europe [5], HOBBIT [6], SAKE [7], Big Data Ocean [8],
SLIPO [9], QROWD [10] and BETTER.
View this announcement on Twitter and the SDA blog:
http://sda.cs.uni-bonn.de/sansa-0-3/
https://twitter.com/SANSA_Stack/status/941643408300441600
Kind regards,
The SANSA Development Team
(http://sansa-stack.net/community/#Contributors)
[1] http://sda.tech
[2] http://sansa-stack.net/downloads-usage/
[3] https://github.com/SANSA-Stack/SANSA-Examples
[4] https://github.com/SANSA-Stack/SANSA-Notebooks
[5] http://www.big-data-europe.eu
[6] https://project-hobbit.eu
[7] https://www.sake-projekt.de/en/start/
[8] http://www.bigdataocean.eu
[9] http://slipo.eu
[10] http://qrowd-project.eu
Dr. Hajira Jabeen
Senior researcher,
SDA, Universität Bonn.
http://sda.cs.uni-bonn.de/people/dr-hajira-jabeen/
Re: SANSA 0.3 (Scalable Semantic Analytics Stack) Released
Posted by Hajira Jabeen <ha...@gmail.com>.
Hi Timur,
Thanks for your interest in SANSA.
The intermediate results are stored in RDDs mostly ( sometimes in Parquet
files).
The use of GraphFrames is in planning, as they are not officially
integrated with spark yet.
Please feel free to contact us in case of any questions.
Regards
Hajira
Dr. Hajira Jabeen
Senior researcher,
SDA, Universität Bonn.
http://sda.cs.uni-bonn.de/people/dr-hajira-jabeen/
On 18 December 2017 at 10:54, Timur Shenkao <ts...@timshenkao.su> wrote:
> Hello
>
> Thank you for very interesting job!
> The question are :
> 1) where do you store final results or intermediate results? Parquet,
> Janusgraph, Cassandra ?
> 2) Is there integration with Spark GraphFrames?
>
> Sincerely yours, Timur
>
> On Mon, Dec 18, 2017 at 9:21 AM, Hajira Jabeen <ha...@gmail.com>
> wrote:
>
>> Dear all,
>>
>> The Smart Data Analytics group [1] is happy to announce SANSA 0.3 - the
>> third release of the Scalable Semantic Analytics Stack. SANSA employs
>> distributed computing via Apache Spark and Flink in order to allow scalable
>> machine learning, inference and querying capabilities for large knowledge
>> graphs.
>>
>> Website: http://sansa-stack.net
>>
>> GitHub: https://github.com/SANSA-Stack
>>
>> Download: http://sansa-stack.net/downloads-usage/
>>
>> ChangeLog: https://github.com/SANSA-Stack/SANSA-Stack/releases
>>
>> You can find the FAQ and usage examples at http://sansa-stack.net/faq/.
>>
>> The following features are currently supported by SANSA:
>>
>> * Reading and writing RDF files in N-Triples, Turtle, RDF/XML, N-Quad
>> format
>>
>> * Reading OWL files in various standard formats
>>
>> * Support for multiple data partitioning techniques
>>
>> * SPARQL querying via Sparqlify (with some known limitations until the
>> next Spark 2.3.* release)
>>
>> * SPARQL querying via conversion to Gremlin path traversals (experimental)
>>
>> * RDFS, RDFS Simple, OWL-Horst (all in beta status), EL (experimental)
>> forward chaining inference
>>
>> * Automatic inference plan creation (experimental)
>>
>> * RDF graph clustering with different algorithms
>>
>> * Rule mining from RDF graphs based AMIE+
>>
>> * Terminological decision trees (experimental)
>>
>> * Anomaly detection (beta)
>>
>> * Distributed knowledge graph embedding approaches: TransE (beta),
>> DistMult (beta), several further algorithms planned
>>
>> Deployment and getting started:
>>
>> * There are template projects for SBT and Maven for Apache Spark as well
>> as for Apache Flink available [2] to get started.
>>
>> * The SANSA jar files are in Maven Central i.e. in most IDEs you can just
>> search for “sansa” to include the dependencies in Maven projects.
>>
>> * There is example code for various tasks available [3].
>>
>> * We provide interactive notebooks for running and testing code [4] via
>> Docker.
>>
>> We want to thank everyone who helped to create this release, in
>> particular the projects Big Data Europe [5], HOBBIT [6], SAKE [7], Big Data
>> Ocean [8], SLIPO [9], QROWD [10] and BETTER.
>>
>> View this announcement on Twitter and the SDA blog:
>>
>> http://sda.cs.uni-bonn.de/sansa-0-3/
>>
>> https://twitter.com/SANSA_Stack/status/941643408300441600
>>
>> Kind regards,
>>
>> The SANSA Development Team
>>
>> (http://sansa-stack.net/community/#Contributors)
>>
>> [1] http://sda.tech
>>
>> [2] http://sansa-stack.net/downloads-usage/
>>
>> [3] https://github.com/SANSA-Stack/SANSA-Examples
>>
>> [4] https://github.com/SANSA-Stack/SANSA-Notebooks
>>
>> [5] http://www.big-data-europe.eu
>>
>> [6] https://project-hobbit.eu
>>
>> [7] https://www.sake-projekt.de/en/start/
>>
>> [8] http://www.bigdataocean.eu
>>
>> [9] http://slipo.eu
>>
>> [10] http://qrowd-project.eu
>>
>>
>>
>> Dr. Hajira Jabeen
>> Senior researcher,
>> SDA, Universität Bonn.
>>
>> http://sda.cs.uni-bonn.de/people/dr-hajira-jabeen/
>>
>
>
Re: SANSA 0.3 (Scalable Semantic Analytics Stack) Released
Posted by Timur Shenkao <ts...@timshenkao.su>.
Hello
Thank you for very interesting job!
The question are :
1) where do you store final results or intermediate results? Parquet,
Janusgraph, Cassandra ?
2) Is there integration with Spark GraphFrames?
Sincerely yours, Timur
On Mon, Dec 18, 2017 at 9:21 AM, Hajira Jabeen <ha...@gmail.com>
wrote:
> Dear all,
>
> The Smart Data Analytics group [1] is happy to announce SANSA 0.3 - the
> third release of the Scalable Semantic Analytics Stack. SANSA employs
> distributed computing via Apache Spark and Flink in order to allow scalable
> machine learning, inference and querying capabilities for large knowledge
> graphs.
>
> Website: http://sansa-stack.net
>
> GitHub: https://github.com/SANSA-Stack
>
> Download: http://sansa-stack.net/downloads-usage/
>
> ChangeLog: https://github.com/SANSA-Stack/SANSA-Stack/releases
>
> You can find the FAQ and usage examples at http://sansa-stack.net/faq/.
>
> The following features are currently supported by SANSA:
>
> * Reading and writing RDF files in N-Triples, Turtle, RDF/XML, N-Quad
> format
>
> * Reading OWL files in various standard formats
>
> * Support for multiple data partitioning techniques
>
> * SPARQL querying via Sparqlify (with some known limitations until the
> next Spark 2.3.* release)
>
> * SPARQL querying via conversion to Gremlin path traversals (experimental)
>
> * RDFS, RDFS Simple, OWL-Horst (all in beta status), EL (experimental)
> forward chaining inference
>
> * Automatic inference plan creation (experimental)
>
> * RDF graph clustering with different algorithms
>
> * Rule mining from RDF graphs based AMIE+
>
> * Terminological decision trees (experimental)
>
> * Anomaly detection (beta)
>
> * Distributed knowledge graph embedding approaches: TransE (beta),
> DistMult (beta), several further algorithms planned
>
> Deployment and getting started:
>
> * There are template projects for SBT and Maven for Apache Spark as well
> as for Apache Flink available [2] to get started.
>
> * The SANSA jar files are in Maven Central i.e. in most IDEs you can just
> search for “sansa” to include the dependencies in Maven projects.
>
> * There is example code for various tasks available [3].
>
> * We provide interactive notebooks for running and testing code [4] via
> Docker.
>
> We want to thank everyone who helped to create this release, in particular
> the projects Big Data Europe [5], HOBBIT [6], SAKE [7], Big Data Ocean [8],
> SLIPO [9], QROWD [10] and BETTER.
>
> View this announcement on Twitter and the SDA blog:
>
> http://sda.cs.uni-bonn.de/sansa-0-3/
>
> https://twitter.com/SANSA_Stack/status/941643408300441600
>
> Kind regards,
>
> The SANSA Development Team
>
> (http://sansa-stack.net/community/#Contributors)
>
> [1] http://sda.tech
>
> [2] http://sansa-stack.net/downloads-usage/
>
> [3] https://github.com/SANSA-Stack/SANSA-Examples
>
> [4] https://github.com/SANSA-Stack/SANSA-Notebooks
>
> [5] http://www.big-data-europe.eu
>
> [6] https://project-hobbit.eu
>
> [7] https://www.sake-projekt.de/en/start/
>
> [8] http://www.bigdataocean.eu
>
> [9] http://slipo.eu
>
> [10] http://qrowd-project.eu
>
>
>
> Dr. Hajira Jabeen
> Senior researcher,
> SDA, Universität Bonn.
>
> http://sda.cs.uni-bonn.de/people/dr-hajira-jabeen/
>