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:

July 7, 2026

Practical Advice for Ontology Engineering on RDF & Property Graphs

Amir Hosseini General data model, knowledge graph, labeled property graph, ontology, ontology engineering, OWL, RDF, semantic layer, Semantic Web, SHACL

Learn how to engineer a graph ontology that keeps its meaning across RDF and property graph systems, with practical modelling best practices and tooling.

June 25, 2026

The Weekly Edge: RDF Inference, Yugabyte MAGE, Visualization with Apache AGE

Amir Hosseini Weekly Edge Apache AGE, knowledge graph, ontology, OWL, Postgres, SHACL

Discover what’s new this week in graph technology: N3 rules for ontology alignment, Yugabyte’s agent-ready Postgres, and ontology-free graph construction.

June 22, 2026

How does gdotv compare to Zazuko Graph Explorer?

Amir Hosseini General graph databases, graph visualization, knowledge graph, RDF, SPARQL

How does gdotv compare against Zazuko Graph Explorer? Follow along with this deep-dive as we explore the various features of each framework.

June 18, 2026

Comparing gdotv vs GraphDB Workbench

Amir Hosseini General graph technology, graph visualization, GraphDB, graphdb workbench, RDF, SPARQL

How does gdotv compare against GraphDB Workbench? Follow along with this deep-dive as we explore the various features of each framework.

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.

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