An ontology-based multicriteria spatial decision support system: a case study of house selection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper proposes an ontology-based multicriteria spatial decision support system (MC-SDSS) for the house selection problem. The house selection ontology serves as a foundation for spatial multicriteria decision analysis (MCDA) in the house selection domain. It is built using the Web Ontology Language (OWL). The ontology represents the spatial MCDA knowledge associated with house selection using semantic machine-interpretable concepts and relationships in such a way that they can be used by machines not just for display purposes, but also for processing, automation, integration, and reuse across applications. It contains concepts (or classes) including quantitative and qualitative criteria (objectives and attributes), decision alternatives (houses for sale), criterion weights, and location attributes of the decision alternatives. The concepts are organized into a hierarchical classification structure using the Analytic Hierarchy Process. To evaluate the decision alternatives, a set of rules is implemented within the OWL knowledge base with the Semantic Web Rule Language. The rules are expressed as combinations of the OWL concepts and their properties. The paper illustrates an implementation of the proposed ontology-based MC-SDSS architecture using a case study of house selection in the City of Tehran, Iran.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it