knowledge-graph-sparql

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.

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.

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.

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.

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.

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.