Building a specialized ontology: Why go on the web?
Bibliographic record
Abstract
This research proposes a comparison of two sources of information for building a specialized ontology: the WWW, a large repository of uncategorized texts, and BioMed, a small specialized corpus in the medical domain. The methodology explored is the use of knowledge patterns. These are explicit markers in text leading to semantic or conceptual relations. Although the method developed has interest for discovering new information in order to enrich the UMLS (a biomedical metathesaurus), we measure its success by an attempt to “rediscover” information already present in the UMLS Metathesaurus. Measures of precision and recall are used in several experiments of instance retrieval for four semantic relations important in the UMLS Methathesaurus, two of a general nature (is-a, synonymy) and two domain specific ones (preventing, inducing). Results show that although the WWW is a noisy repository, its exploration has potential and does allow the discovery of valuable specialized knowledge.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.048 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".