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joseph-higaki/README.md

👨🏾‍💻 Joseph Higaki

Knowledge Graph & Data Engineer

I turn messy relational and lakehouse data into something you can actually query and extract insights from.

Right now I'm working on ontology and knowledge-graph adoption at a global pharmaceutical company. My portfolio spans virtual knowledge graphs, biomedical GraphRAG, and SHACL-governed semantic pipelines. Background across regulated industries: pharma, banking, and telecom.

🔧 What I'm building

TOP 3, ORDER BY pride desc. The rest are pinned below 👇

  • Virtual Knowledge Graph Connectivity Ontop answers SPARQL over Trino against Postgres and Iceberg, staged in "rungs" and validated against a GraphDB ground truth. Why it matters: exposes relational + lakehouse data as a queryable graph without copying it (OBDA) and proves fidelity instead of assuming it.

  • Biomedical GraphRAG Bench Benchmarks vector RAG against RDF GraphRAG retrievers on biomedical question sets, measuring the hop-count vs. recall tradeoff in a reproducible Docker harness. Why it matters: answers the question a pharma AI team actually asks: when does graph retrieval beat vectors? With numbers, not vibes.

  • Resume Graph My résumé as an RDF knowledge graph: SHACL-validated, published as an interactive SPARQL site, and projected into tailored CV variants per application. Why it matters: a full semantic stack end-to-end, with a SHACL gate that makes unevidenced skill claims impossible to publish. (The PDF version of my CV falls out of this.)

🛠 Tools & tech

  • Languages: Python, SQL, SPARQL, Cypher
  • Knowledge graphs & semantics: RDF, OWL, SHACL, ontology engineering, Neo4j, GraphDB, Ontop / OBDA, metaphactory
  • Data engineering: Airflow, dbt, Docker, Terraform, DuckDB
  • Warehouses & stores: BigQuery, PostgreSQL, Apache Iceberg, Trino, Redshift, Snowflake, Cassandra
  • Cloud: AWS, GCP

Certifications: Neo4j Certified Professional · metaphacts (AI Metis Engineer, KG App Building, Visual Modeling for KG Engineers) · AWS Certified AI Practitioner · Claude Certified Architect – Foundations

🌱 Where I'm headed

  • Deeper into knowledge-graph engineering: OBDA / virtual graphs, ontology governance, and graph-native retrieval for AI.
  • Building things that put semantic data to work in regulated, real-world domains.

📫 Let's connect

Pinned Loading

  1. virtual-knowledge-graph-connectivity virtual-knowledge-graph-connectivity Public

    Virtual Knowledge Graph plumbing test

    Python

  2. rag-bench-analytics rag-bench-analytics Public

    This repo turns accumulated benchmark results into a dimensional model and a dashboard*

    Python 1

  3. resume-graph resume-graph Public

    Python

  4. emr_data_pipeline emr_data_pipeline Public

    Electronic Medical Records Ingestion, Analytics features and Dashboard

    Python 1

  5. biomedical-rag-bench biomedical-rag-bench Public

    An evolving evaluation harness for retrieval-augmented generation over biomedical knowledge

    Python

  6. patient_conditions_weather patient_conditions_weather Public

    Data Extraction and Analysis for Clinical conditions along with weather information at the timeof diagnosis

    Jupyter Notebook