Validating ontologies in informatics systems: approaches and lessons learned for AEC
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
In their pursuit to represent a human-savvy machine interpretable model of knowledge, informatics ontologies span three dimensions: philosophy, artificial intelligence, and linguistics. This poses several challenges to ontology validation. Within the scope of knowledge models, four types of validity are relevant: statistical, construct, internal and external. Based on benchmarking some tools and best practices from other domains, a map is proposed to link specify a set of tools to support the handling of four validity types (statistical/conclusion, internal, construct, and external) in each of the three dimensions. The map advocates a debate-based approach in validating the philosophical dimension to allow for innovation and discovery; use of competency questions and automated reasoning tools for the artificial intelligence dimension; and experimenting with lexical analysis tools (especially web contents) for the linguistic dimension. A set of best practices are proposed based on benchmarking other domains. These include falsifying the conceptual frameworks of research methodologies, scope management, iterative development, adequate involvement of experts, and peer review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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