Housing Passport knowledge graph: Promoting a circular economy in urban residential buildings
Bibliographic record
Abstract
• Data-driven circular economy decision-making promotes sustainable housing. • Housing Passports (HP) support circularity by standardizing housing characteristics. • HP knowledge graph (HPKG) contextualizes and semantically links heterogenous data. • HPKG supports the scalability and interoperability of housing data. • 62 million housing datapoints across various scales and contexts are presented. This paper introduces the Housing Passport knowledge graph (HPKG) as a novel digital standardization framework with a robust semantic data infrastructure to promote a circular economy in the home-building industry. Unstandardized and dispersed housing data impedes a comprehensive assessment of housing stock characteristics and life cycle impacts, hindering the implementation of circular economy principles. The HPKG addresses this challenge by providing (1) a standardized framework for integrated analysis of residential buildings’ affordability and circularity across various spatiotemporal scales and socioeconomic contexts, and (2) a scalable semantic infrastructure using web ontologies that enhances the sharability, searchability, readability, and interoperability of housing-related data. A case study involving five Canadian cities demonstrates the HPKG's effectiveness in semantically linking and standardizing approximately 62 million data points representing over 1.2 million residential buildings. The results show how the HPKG enables a multi-scale integrated assessment of Canadian housing stock, focusing on affordability, energy efficiency, and environmental footprints. As a key conclusion, the HPKG supports informed decisions regarding housing stock by enabling the exploration of circular economy scenarios that prioritize the reuse and recycling of residential building materials. The HPKG empowers stakeholders to develop residential typologies that promote affordability, circularity, and sustainability across diverse socioeconomic contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".