Amir Hosseini

About Amir Hosseini

Amir holds a PhD in Computer Science from Herriot-Watt University and recently completed his postdoctoral fellowship in Large-Scale Nested Data Processing the University of Edinburgh's School of Informatics. He brings several years of experience in graph-based structures and semantic web technologies to the team, with a focus on data processing and validation. As a long-time RDF user, he leads the continuous design and implementation of solutions for RDF/SPARQL users at gdotv.

Posts by Amir Hosseini:

June 8, 2026

Building a Property Graph Pipeline from the TEG Benchmark: Part 3 – Exploring with gdotv

Amir Hosseini General AWS Neptune, benchmark, Cypher, graph data, graph databases, graph visualization, Gremlin

Explore four queries in gdotv against TEG-DB show what edge text enables: sentiment polarity, citation chains, and contradictions invisible to graph topology.

June 5, 2026

Building a Property Graph Pipeline from the TEG Benchmark: Part 2 – Populating the AWS Neptune Graph

Amir Hosseini General AWS Neptune, benchmark, graph data, graph databases, graph visualization, Gremlin, S3

Load TEG-DB CSV files into Amazon Neptune via the bulk loader API, connect gdotv with IAM auth, and verify the loaded graph with Gremlin.

June 2, 2026

Building a Property Graph Pipeline from the TEG Benchmark: Part 1 – From HuggingFace to Structured CSV

Amir Hosseini General AWS Neptune, benchmark, citation graph, Cypher, graph databases, graph visualization, Gremlin

Find out how to build a property graph pipeline converting the TEG-DB graph benchmark from PyTorch Geometric pickles into AWS Neptune import-ready CSV files.

April 28, 2026

SHACL in gdotv: Unifying Your RDF Data Model & Validation

Amir Hosseini RDF, Tech Feature Apache Jena Fuseki, data model, database schema, knowledge graph, ontology, OWL, RDF, RDFS, SHACL, triplestore

Discover how gdotv transforms SHACL validation shapes into a unified RDF data model by integrating ontology and instance data into one explorable view.

March 18, 2026

Evaluating Codebase-Oriented RAG through Knowledge Graph Analysis [Part 2]

Amir Hosseini Tech Feature C++, Cypher, graph data, graph databases, graph visualization, knowledge graph, LLMs, Memgraph, Python, RAG

Crack open your codebase RAG with a knowledge graph analysis that illustrates hidden dependencies and other structural breakdowns in part 2 of this series.

March 11, 2026

Evaluating Codebase-Oriented RAG through Knowledge Graph Analysis [Part 1]

Amir Hosseini Tech Feature Cypher, graph analysis, graph databases, graph visualization, knowledge graph, LLMs, Memgraph, Ollama, RAG, SQLite

Crack open your codebase RAG with a knowledge graph analysis that illustrates hidden inheritance chains, hub detection, and more in this 2-part blog series.

February 12, 2026

SPARQL Query Guardrails: Making Your SPARQL Queries ETL-Ready

Amir Hosseini RDF, Tech Feature Apache Jena Fuseki, connected data, ETL, healthcare, life sciences, ontology, RDF, semantic layer, SPARQL, triplestore

Discover how SPARQL query guardrails in the gdotv IDE help you avoid common mistakes, pitfalls, and typos in your SPARQL queries *before* you execute them.

November 19, 2025

ISWC Recap: Trends & Reflections from the International Semantic Web Conference 2025

Amir Hosseini General, RDF AI, ISWC, knowledge graph, LLMs, RDF, RDFox, semantic technology, Semantic Web, SPARQL, triplestore

Catch up on everything that happened at ISWC this year, including awards, papers, trends, and reflections on what this year’s conference means for the future.

November 14, 2025

The Weekly Edge: RDF Edition! [14 November 2025]

Amir Hosseini Weekly Edge ISWC, knowledge graph, ontology, OWL, RDF, Semantic Web, SHACL, SPARQL, triplestore, Weekly Edge

Discover what’s new in the world of RDF triplestores this week, including beginner tutorials, ISWC coverage, and how RDF powers the Netflix knowledge graph.

November 12, 2025

RDF Support Is Now Available in G.V() [v3.41.99 Release Notes]

Amir Hosseini General, Release Notes AllegroGraph, AWS Neptune, graph databases, QLever, RDF, Semantic Web, SPARQL, Stardog, Tentris, triplestore, Virtuoso

Learn more about how the latest release of G.V() now supports RDF triplestores and the SPARQL query language with autocomplete, visualization, and more.