Run a SPARQL query across multiple mutually-distrusting RDF data holders (federated MPC) — per-holder local sub-evaluation, crypto-free disclosed-key global-IRI joins, and an honest-majority Shamir secret-sharing backend for secure cumulative aggregates + hidden-value (private-key) joins. Use for confidential federated SPARQL / secret-shared aggregates / private set-intersection joins over RDF. EARLY/RESEARCH, native-only; honest-majority with RS-consistency-checked reconstruction (tamper DETECTION + abort, and robust correction where redundancy allows) — semi-honest is the floor when no redundancy exists (e.g. the degree-2t equality open at n=2t+1, detection-only); NOT dishonest-majority / not full malicious security. Collaborative ZK proof of correctness+attestation is a stub.
How to USE the sparq RDF triplestore + SPARQL 1.1/1.2 engine (Rust crates, CLI, HTTP server, Python, JS/WASM) and its capabilities (reasoning, SHACL, full-text, vector, GeoSPARQL, streaming RSP-QL, ZK query proofs). Router skill — read this first, then the per-surface skill that matches the task.
Use when you need RDFS, OWL 2 RL, or Notation3/EYE-rule entailment over a sparq RDF graph — materialize the deductive closure (forward-chaining), query the entailed triples, maintain the closure incrementally under inserts/deletes, get derivation proof-trees (why()), or check OWL inconsistency; backed by the sparq-reason crate. For the COMPLETE OWL 2 EL class-subsumption lattice (which RL is sound-but-incomplete for), use the separate opt-in sparq-reason-el classifier (CR1-CR5 saturation). For OWL 2 QL certain-answer query rewriting (PerfectRef over DL-Lite_R, EXPERIMENTAL, fail-closed CQ-shape gate), use the separate opt-in sparq-reason-ql crate.
Prove and verify a SPARQL query result over committed RDF Verifiable Credentials in zero knowledge with sparq-zk + sparq-zk-compose — per-graph Poseidon2 commitments, BGP scan + integer FILTER Noir proofs, issuer Schnorr attestation (incl. hidden-key set membership), status-list revocation (clear- or hidden-index), verifier nonces / single-use replay defence, and the ProofManifest/circuit family. Use when building or driving the zk-query-proofs surface (proving a query answer, verifying a manifest, attesting an issuer, checking revocation). Requires the Noir toolchain (nargo + bb).
Validate RDF data against SHACL shapes with the sparq engine: SHACL Core constraints (class, datatype incl. the SHACL-1.2 disjunctive list form, cardinality, ranges, paths, logical, node/property, qualified, closed incl. sh:ByTypes, in/hasValue, the SHACL-1.2 list constraints sh:memberShape / sh:uniqueMembers / sh:min+maxListLength / sh:uniqueValuesFor, and the SHACL-1.2 value constraints sh:subsetOf / sh:someValue / sh:singleLine / sh:rootClass with path-valued sh:equals/disjoint/lessThan comparands and severity-threshold sh:conforms), SHACL-SPARQL sh:sparql constraints (§5.2), and custom SPARQL-based constraint components (sh:ConstraintComponent, §6) — then read the conformance/violations validation report as N-Triples, deterministic JSON, W3C report-vocabulary Turtle, or human text. Also runs opt-in SHACL Advanced Features (SHACL-AF) rules — sh:rule (sh:TripleRule + sh:SPARQLRule) — to INFER triples (feature `shacl-af`), and assembles the shapes graph itself — sh:shapesGraph discovery + transitive owl:impo
Training-free structural entity similarity over a sparq RDF graph with the opt-in sparq-sim crate: build an entity's (direction, predicate, neighbor) signature straight from the store's permutation indexes, score weighted Jaccard, Dice, or overlap similarity between two entities, and retrieve the top-k most-similar entities (most_similar) via index-driven candidate generation — no embeddings, no model, no training, correct under incremental updates. Use when adding co-citation / shared-context similarity, predicate-profile (role) similarity, entity/relation linking, or the structural half of a hybrid (structural + text-vector) retrieval over a sparq Graph.
Cost-based federated SPARQL source selection + bind-vs-hash join planning over already-fetched source descriptors, plus an ANAPSID-style non-blocking streaming join with operator spill, via the opt-in sparq-fedplan crate. Use when planning a federated BGP across multiple SPARQL endpoints from their served statistics (VoID property/class partitions + mined scs: characteristic sets): deciding which sources can contribute to each triple pattern (HiBISCuS recall-safe pruning + CostFed skew-aware cardinality), choosing a join order with per-join bind-vs-hash-vs-streaming algorithm selection (characteristic-set star cardinality for intermediate sizes), and executing a memory-bounded non-blocking symmetric hash join over incrementally-arriving sub-results (StreamJoin, spill to a backing store, result multiset-equal to a blocking join). Pure + deterministic planning, no network I/O. Off by default; does not touch sparq-core/sparq-engine's lean build. Also covers live adaptive RE-planning at stage boundaries (mid-exec
Use when an LLM/agent should access a sparq RDF dataset over the Model Context Protocol (MCP) as first-class tools — run SPARQL queries, mine the dataset schema, list class profiles or namespace prefixes, read stats or a VoID descriptor, optionally SHACL-validate against caller-supplied shapes or derive a shape-aware describe_form FormDescription for a focus node, and (gated, off by default) apply SPARQL updates. Covers the opt-in sparq-mcp crate — a JSON-RPC 2.0 MCP server (initialize, tools/list, tools/call) exposing query (SELECT/ASK to SPARQL-JSON), construct (CONSTRUCT/DESCRIBE to N-Triples), introspect (effective schema as JSON or token-budgeted text), stats, classes (class IRIs with instance and predicate counts), prefixes (namespace declarations and term counts), void (W3C VoID N-Triples), an opt-in read-only validate tool, and a gated update tool that is OFF by default; the transport-agnostic handle_message dispatch core plus the optional stdio feature; and the honest trust model (a local agent-tool