Article Semanticizer - stitching data mining services into a standalone search appliance
Bibliographic record
Abstract
The Biodiversity Heritage Library, traditional publishers, scientific societies, and many other organizations with large or small collections of unstructured biological texts need a simple, scalable mechanism to create search indices. These indices are immediately valuable to the public and also afford opportunities to enrich their content through internal and external crosslinks. Here, we describe an MIT-licensed application that combines the strengths of two Global Names, http://www.globalnames.org services, another from the Encyclopedia of Life, http://eol.org and a third from AlchemyAPI, http://www.alchemyapi.com/ . In combination, these services discover and resolve scientific names in raw text (or images), expand these to their vernacular equivalents in multiple languages, and extract known entites such as surnames, placenames (with geographic coordinates from GeoNames), and organization names. The resultant database of indexed terms and the full text are then ingested into ElasticSearch for immediate autocomplete and fulltext search capabilities. A proof-of-concept was constructed from back issues of The Canadian Entomologist (1868-2002) and temporarily made available at http://canent.shorthouse.net . Code is available at https://github.com/dshorthouse/article_semanticizer .
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.030 |
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".