knowledge-graph-sparql

Build RDF knowledge graphs from research metadata and query them with SPARQL.

33|6|Updated Mar 17, 2026

Search Tags:#knowledge graph #sparql #rdf #wikidata #linked data #entity linking #ontology reasoning

Ready to run

npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill knowledge-graph-sparql

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: knowledge-graph-sparql
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/21-library-science/knowledge-graph-sparql
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill knowledge-graph-sparql

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires rdflib, requests, pandas, networkx, matplotlib, numpy.

What problem does it solve?

This Skill helps you transform library and research data into a structured knowledge graph and then extract answers from it using SPARQL, including entity linking and multi-hop reasoning across heterogeneous metadata.

Core Features & Use Cases

Build RDF knowledge graphs: Convert tabular or structured scholarly data into RDF triples with consistent namespaces and identifiers.
Query with SPARQL (local and federated): Run SELECT/CONSTRUCT-style queries over a local triple store and federate queries to external endpoints like Wikidata.
Entity linking & reconciliation: Map author and concept mentions to canonical URIs (e.g., ORCID/Wikidata), enabling disambiguation and authority alignment.
Reasoning and visualization: Apply basic RDFS/OWL-style inference patterns and visualize graph topology for analysis.

Quick Start

Use this skill to construct an RDF graph from your research metadata, run SPARQL queries to answer multi-hop questions, and optionally enrich entities via Wikidata lookup.

Frequently Asked Questions about knowledge-graph-sparql

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

FAQPage Schema

How do I convert tabular research metadata into an RDF knowledge graph?▼

Convert tabular research metadata into an RDF knowledge graph by mapping data rows to RDF triples with consistent namespaces and identifiers using rdflib. This process structures scholarly datasets for SPARQL querying and entity linking.

How does federated SPARQL querying work with Wikidata and local knowledge graphs?▼

Federated SPARQL querying combines local triple store queries with remote endpoint retrieval from Wikidata or DBpedia. You execute SELECT or CONSTRUCT queries across local RDF graphs while pulling external authority data for reconciliation.

Can I map author mentions to ORCID and Wikidata URIs for entity reconciliation?▼

Yes, entity linking maps author and concept mentions to canonical URIs like ORCID and Wikidata. This reconciliation process enables disambiguation and authority alignment across heterogeneous scholarly metadata datasets.

What is the best way to visualize knowledge graph topology for research data analysis?▼

Visualize knowledge graph topology by applying networkx and matplotlib to render and analyze graph structure derived from RDF data. This reveals multi-hop entity relationships and scholarly connection patterns for research analysis.

Does rdflib support RDFS and OWL ontology reasoning for multi-hop inference?▼

Yes, rdflib supports applying basic RDFS and OWL-style inference patterns to RDF knowledge graphs. This ontology-driven reasoning enables multi-hop retrieval and answers complex queries across linked scholarly datasets.

What are the limitations of using SPARQL for entity linking across heterogeneous metadata?▼

SPARQL entity linking across heterogeneous metadata requires consistent namespaces and canonical URI mapping to prevent disambiguation errors. Federated queries also depend on remote endpoint availability and schema alignment for successful authority reconciliation.