ISWC 2026 · Posters & Demos · Bari, Italy
Three knowledge graphs — up to 60 GB and 3.8 billion triples — published as plain files on static hosting, with no server behind them. Query them here, three ways, and watch what each answer physically cost.
A static page with the engine compiled to WebAssembly. Open a graph you did not build, read its card, run a summary-safe query that never touches the index, then a selective one — and watch the run strip report the requests issued, the bytes moved, the permutation chosen and the exact byte ranges read.
The same URLs, from Python — no kernel, no server, no local install.
Pyodide runs in the page and rete-graph installs from
PyPI; results land in pandas, and stats() keeps counting
the bytes and range requests each step actually spent.
A packaged MCP extension puts the same engine inside an MCP-capable assistant: nine tools over these files, among them the card, the schema and SPARQL. It reads the card to learn the vocabulary before querying — which is what stops a model inventing predicate names.
Publishing an RDF graph for interactive use normally means running a SPARQL
endpoint or shipping the whole dataset to whoever asks. These three take a
third route: the graph, its dictionary, its six permutation indexes and its
own description are packed into one immutable file whose sections
are addressed by byte range. A .rete opens with a 1 KB
header holding a typed section directory — the offset and length of
everything else — so nothing later in the file is ever found by scanning and
every read is a bounded HTTP Range request.
The dataset card and the schema pyramid sit before the dictionary, so a visitor can learn what a graph contains — title, licence, provenance, exact per-predicate and per-class histograms, runnable starter queries — without opening a triple index at all. Because all six permutations exist, a triple pattern with bound terms resolves to a contiguous range with its bound components leading, which is what keeps remote routing bounded.
All three are plain objects on Cloudflare R2 behind a CDN — 206 Partial Content, CORS, no token, no redirect. The same URLs feed all three stations.
| Graph | Triples | File | Licence | Source |
|---|---|---|---|---|
crossref
The complete Crossref March 2026 citation graph: 179.5M registered works and 2.0B cites edges — the largest single .rete published. |
3,777,727,303 | 60.2 GB | CC BY 4.0 | Public Data File |
| zenodo-records Every published Zenodo record (7.76M) as a DataCite scholarly graph, with ORCID creators and version chains. | 215,396,999 | 2.09 GB | CC-BY-4.0 | Zenodo exporter |
| open-pulse The EPFL/SDSC research-software graph — repositories, people, labs and their ROR institutions. A third-party graph, not ours. | 3,697,829 | 49 MB | GitHub metadata · ontology © SDSC-ORDES | Open Pulse |
They share canonical IRIs — a work is https://doi.org/…, a
person is https://orcid.org/… — so Crossref and Zenodo join on
DOIs and ORCIDs without any owl:sameAs reconciliation.
Measured on 2026-07-24 against the public files, over a residential link,
end to end — process start and TLS included. Reproducible with the shipped
rete card-url, sparql-url, why-url
and cost commands.
Interaction on zenodo-records.rete |
Requests | Bytes read | % of file | Index read | Time |
|---|---|---|---|---|---|
| Dataset card | 3 | 49,452 | 0.002% | no | 0.7 s |
| Predicate histogram, exact | 3 | 49,452 | 0.002% | no | 0.7 s |
| Selective pattern, 19 triples | 41 | 7,602,176 | 0.36% | partial | 5.7 s |
| Join: version chain → title | 30 | 17,825,792 | 0.85% | partial | 4.3 s |
| Join: ORCID author → works | 50 | 18,743,296 | 0.89% | partial | 7.3 s |
| Join: cited-by, empty result | 24 | 4,980,736 | 0.24% | partial | 3.4 s |
The card's 36 per-predicate counts sum to exactly 215,396,999 — the histogram is exact, not sampled. And the entry cost barely grows with the graph: the same three requests return 2.6–2.8 KB on the 17.5 GB ORCID and 35.9 GB OpenCitations files.
Where the model stops paying off. Bounded, kilobyte-scale access is guaranteed for summary-safe questions and typical for selectively routed ones. An unselective aggregate over a whole graph must read the index and will move a large fraction of the file — the demonstration lets you trigger that too, and watch it happen.
Snapshots are also immutable: updates mean a rebuild. This is a publication format, not a transactional one.
The core is a single Rust library compiled natively and to WebAssembly.
These wrap it. They differ in language and runtime but not in what they do:
each opens a local .rete or a remote URL through the same range
interface, and none of them needs a server.
pip install rete-graph…or in Pyodide — JupyterLite, marimo, any browser kernel:
%pip install rete-graphCPython ≥ 3.9 everywhere. In Pyodide the same package resolves a PyEmscripten wheel straight from PyPI and runs in the page — no kernel, no server. That is exactly what station (b) above is doing.
npm install rete-graphNode ≥ 18 and browsers, via a bundler or one <script> tag. This is the engine the console runs.
cargo add rete-core --git https://github.com/caviri/reteThe core library and the CLI. Anywhere Rust runs, native or wasm.
mvn -f clients/java installio.github.caviri:rete-client (wasm on Chicory) and rete-rdf4j, a read-only RDF4J Repository. Not on Maven Central yet.
remotes::install_github("caviri/rete", subdir = "clients/r")R ≥ 4.2 plus a Rust toolchain. Results come back as data frames.
/plugin marketplace add caviri/reteNine tools over these files in any MCP host. Or install the packaged .mcpb from station (c) above.
Download the add-on zip, then in Blender:
Edit → Preferences → Add-ons → Install… and pick
rete-*.zip. Enable it and the panel appears in the 3D view's
sidebar.
Blender ≥ 4.2, engine bundled. SPARQL results become 3D scenes.
Registries where they exist, source where they do not — the Rust crate, the Java modules and the R package are built from the repository until their first release. The full matrix, with API docs for each, is on the project site.
.rete: Browser-Native SPARQL over Static, Range-Addressable RDF Files. ISWC 2026 Posters & Demonstrations Track, Bari, Italy, 25–29 October 2026.
Read the paper (PDF) Source code (Apache-2.0) Full project site
This site is a frozen snapshot built for the demonstration: three graphs, one console, one notebook, one extension. The live project — 80+ published graphs, the CLI, and clients for JavaScript, Python, R, Java and Blender — continues at caviri.github.io/rete.