Validating ontologies in informatics systems: approaches and lessons learned for AEC
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.
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 | 0.076 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.018 | 0.043 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".