skills.rest
sparq-org
sparq-orgOFFICIAL
Explore all skills by this author →
GitHub

structural-similarity

Measure structural similarity between RDF entities using predicate-IDF-weighted Jaccard similarity.

8|1|Updated Jun 7, 2026
Search Tags:#Rust#SPARQL#RDF#entity linking#entity similarity
One-click install
npx skills add https://github.com/sparq-org/sparq --skill structural-similarity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: structural-similarity
Source: https://github.com/sparq-org/sparq/tree/main/skills/structural-similarity
Command: npx skills add https://github.com/sparq-org/sparq --skill structural-similarity

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of comparing RDF entities for structural similarity without the need for embeddings, models, or training, providing a direct method for entity comparison within a Sparq RDF graph.

Core Features & Use Cases

  • Entity Signature: Build an entity's structural signature using permutation indexes from the Sparq RDF graph.
  • Similarity Scoring: Score the structural similarity of two entities using predicate-IDF-weighted Jaccard similarity.
  • Top-K Retrieval: Retrieve the top-k most similar entities using index-driven candidate generation.
  • Use Case: Use this Skill for tasks like entity linking, co-citation analysis, and shared-context similarity detection in a Sparq graph.

Quick Start

To find the top-10 most similar entities to the entity with URI 'http://example.org/bolt', run the following command:

cargo run --release -p sparq-cli -- query data.ttl turtle \
  'SELECT ?s ?o WHERE { ?s <http://schema.org/name> ?o } LIMIT 10'

Frequently Asked Questions about structural-similarity

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I measure RDF entity similarity without using embeddings?▼

You can measure RDF entity similarity without embeddings by using predicate-IDF-weighted Jaccard similarity to compare structural signatures built from graph features, entirely avoiding model training.

What is the best way to find similar entities in an RDF graph for entity linking?▼

Finding similar entities for entity linking is best handled through index-driven candidate generation, retrieving the top-k structurally similar RDF entities based on shared-context analysis.

Can I use predicate-IDF-weighted Jaccard similarity for SPARQL graph data?▼

Yes, you can use predicate-IDF-weighted Jaccard similarity for SPARQL graph data by leveraging permutation indexes to build structural signatures for direct entity comparison.

Do I need to train a model before comparing RDF entities for structural similarity?▼

No, you do not need to train a model before comparing RDF entities, because structural similarity is calculated directly from graph features using weighted Jaccard scoring without embeddings.

How do I retrieve the top-k most similar RDF entities in a Sparq graph?▼

You retrieve the top-k similar RDF entities by running a Sparq-cli query against your turtle data, utilizing index-driven candidate generation to rank and return matching structural signatures.

When should I avoid using embedding-based methods for entity similarity detection?▼

You should avoid embedding-based methods when you need direct co-citation analysis or shared-context similarity detection in RDF datasets without the overhead of generating vector models.

Related Skills

Explore More Software Engineering Skills→
sparq-orgsparq-org
8

data-formats

Ingest RDF data into sparq Graphs from Turtle, N-Triples, N-Quads, and TriG formats.

Official
Advanced
cdeistopenedcdeistopened
2

wiki-extract-entities

Extract named entities from transcripts into JSON inventories with counts and sentiment.

Community
Advanced
sparq-orgsparq-org
8

query-pkg

Retrieve facts from the Project-Knowledge-Graph using SPARQL queries.

Official
Advanced
an8079an8079
4

embedding-generation

Train and optimize embedding models for text, images, and multimodal data.

Community
Advanced