Applications using Jelly
If you are using Jelly in your project, we would be glad to list it here! Please open an issue or open a pull request with the information on your use case.
Jelly-JVM
- jelly-cli – simple but performant command-line utility for working with Jelly files.
- The app can be used to convert to/from Jelly, validate and debug Jelly files.
- You can find the code and released binaries on GitHub. The repository also has up-to-date installation instructions and command usage examples.
- You can also use the Jelly-RDF/setup-cli GitHub Action to quickly set up jelly-cli in your CI/CD workflows.
- RMLMapper – library and CLI tool for generating knowledge graphs from semi-structured data, using RML rules.
- RMLMapper supports Jelly as one of the output formats. You can use it to convert CSV, JSON, Excel, SQL, and other data sources into Jelly.
- nanopub-java – Java library for working with nanopublications.
- The library supports Jelly as one of the serialization formats, including processing streams of multiple nanopublications.
- TopBraid SHACL – library and CLI tool for validating RDF and reasoning with SHACL shapes.
- The CLI tool supports reading and writing Jelly files by setting the format to
jelly.
- The CLI tool supports reading and writing Jelly files by setting the format to
pyjelly
- YATTER – a library and CLI tool that translates between YARRRML documents and their corresponding RML or R2RML representations.
- YATTER CLI supports Jelly as one of the input/output formats for RML/R2RML mappings.
- Morph-KGC – a Python engine for materializing RDF knowledge graphs from heterogeneous data sources using RML and R2RML.
- Morph-KGC can output graphs in the Jelly format via RDFLib and the
pyjellyplugin (optional extra:pip install "morph-kgc[jelly]").
- Morph-KGC can output graphs in the Jelly format via RDFLib and the
- KGX – Python library and CLI tool for transforming and exchanging Biolink knowledge graphs across multiple formats.
- Supports reading and writing the Jelly RDF serialization format via
pyjelly.
- Supports reading and writing the Jelly RDF serialization format via
- kglab – Python library for building and analyzing knowledge graphs.
- Supports loading and saving RDF graphs in the Jelly RDF serialization format via
pyjelly.
- Supports loading and saving RDF graphs in the Jelly RDF serialization format via
Projects
- Nanopub Registry and Nanopub Query – next-gen services for distributed storage, management, and querying of nanopublications.
- Jelly is used for communication between the services in the nanopub network. Jelly endpoints (ending with
.jelly) are also exposed for downstream applications to consume nanopubs. - The Registry stores nanopublications in the Jelly format and uses a Jelly transcoder to merge nanopubs on the fly into a single stream.
- Jelly is used for communication between the services in the nanopub network. Jelly endpoints (ending with
- RiverBench benchmark suite.
- Jelly is used as one of the serialization formats for distributing datasets in RiverBench.
- Jelly is also used for distributing the RDF metadata of benchmark datasets, tasks, and other resources.
- This is implemented in the ci-worker application – a Scala 3 program making heavy use of Jelly-JVM's streaming capabilities.
- Jelly-JVM benchmark code. This code was used to produce the results seen on the performance page.
- RDF Stream Taxonomy (RDF-STaX) uses Jelly for distributing the RDF-STaX ontology and the living literature review of RDF streaming.
Example datasets in the Jelly format
Below listed are some example datasets available in the Jelly format. All of these are in the delimited format. The licenses for these datasets are specified on the linked documentation pages.
-
Toy dataset (starting example –
foaf.jelly):- foaf.jelly
- foaf.ttl – same file in the Turtle format.
- foaf.jelly.txt – text representation of the Jelly file, for debugging purposes.
-
Large datasets (millions of triples/quads):
- ASSIST-IoT weather measurements (documentation) – 80 million triples.
- 5 million nanopublications (documentation) – 171 million quads.
- RDF-star annotated facts from YAGO (documentation) – 2 million triples.
-
Small datasets:
- RDF-STaX ontology (documentation).
- RiverBench suite metadata (documentation) – RiverBench also includes metadata in Jelly for benchmark tasks, datasets, and more.
You can find some more interesting datasets in the Jelly format on the RiverBench website.
Commercial support
NeverBlink provides commercial support services for Jelly, including implementing custom features, system integrations, implementations for new languages, benchmarking, and more.