Effectiveness of Lexico-syntactic Pattern Matching for Ontology Enrichment with Clinical Documents
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
OBJECTIVE: To evaluate the effectiveness of a lexico-syntactic pattern (LSP) matching method for ontology enrichment using clinical documents. METHODS: Two domains were separately studied using the same methodology. We used radiology documents to enrich RadLex and pathology documents to enrich National Cancer Institute Thesaurus (NCIT). Several known LSPs were used for semantic knowledge extraction. We first retrieved all sentences that contained LSPs across two large clinical repositories, and examined the frequency of the LSPs. From this set, we randomly sampled LSP instances which were examined by human judges. We used a two-step method to determine the utility of these patterns for enrichment. In the first step, domain experts annotated medically meaningful terms (MMTs) from each sentence within the LSP. In the second step, RadLex and NCIT curators evaluated how many of these MMTs could be added to the resource. To quantify the utility of this LSP method, we defined two evaluation metrics: suggestion rate (SR) and acceptance rate (AR). We used these measures to estimate the yield of concepts and relationships, for each of the two domains. RESULTS: For NCIT, the concept SR was 24%, and the relationship SR was 65%. The concept AR was 21%, and the relationship AR was 14%. For RadLex, the concept SR was 37%, and the relationship SR was 55%. The concept AR was 11%, and the relationship AR was 44%. CONCLUSION: The LSP matching method is an effective method for concept and concept relationship discovery in biomedical domains.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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