Matchmaking through semantic annotation and similarity measurement
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 proposed work briefly describes an approach to automatically extract structured information from semi-structured documents to match the document creators and users in order to find the best similarities between them and connect them for further collaborations. The general idea is to employ a semantic annotation technique and similarity measurement approach by using the ontology to find best matches between web documents. The proposed approach uses ontologies to annotate the extracted information and for the measuring the similarity between each pair of documents. GATE (General Architecture for Text Engineering) as one of the most famous annotation tools has been utilized to annotate semi-structure documents. A novel algorithm is proposed to update the supported ontology for extraction purpose in GATE by using a training data set. Furthermore, specific domain-based metrics are also utilized to measure semantic similarities between documents with regard to semantic annotations which are implemented in an ontology-based approach. These metrics can be used in order to find the most similar web documents among documents corpus.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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