Fernwood Agentic Knowledge Graph for Housing Innovation

Canada’s housing crisis has prompted both government and industry to call for technological innovation in the building permit and development approval process, where regulatory complexity, fragmented policy documents, and inconsistent interpretation contribute to costly delays. Fernwood Agentic is responding to this need by building an AI system that helps architects, planners, and developers navigate municipal housing and zoning policy more quickly and accurately. This Mitacs project focuses on a core component of that system: a knowledge graph that captures the rules, zones, restrictions, and cross-references found across the City of Victoria’s policy documents and represents them as a structured, queryable network. Two interns will catalog Victoria’s policy corpus, design a schema defining the types of entities and relationships it contains, build an LLM-based pipeline to extract that information from the source documents, populate a Neo4j graph database, and validate the results with domain experts including architects, planners, and City of Victoria staff. The expected benefit to Fernwood Agentic is a set of core technical assets, including the populated knowledge graph, a reusable extraction pipeline, and a formal schema specification, that will improve the accuracy and reliability of its RAG system and serve as the blueprint for expanding to additional municipalities across British Columbia.

Faculty Supervisor:

Jeff Edmonds

Student:

Partner:

Fernwood Agentic

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

York University

Program: