USER INTERFACES - Powering Linked Open Data applications with Fedora and Islandora CLAW
Bibliographic record
Abstract
Repositories have traditionally focused on storing content and metadata for use by local applications and services, but this is a poor fit for the world of linked open data. Fedora, the flexible, extensible, open source repository platform, has been designed and implemented as not just a repository but a linked data server. This has been accomplished primarily through alignment with the Linked Data Platform recommendation from the W3C, but Fedora also has a formally specified REST API that aligns with a variety of modern web standards, such as Memento, Web Access Control, and Activity Streams 2.0. This focus on linked data and web standards has allowed Fedora to serve as a reliable repository that also powers web-based linked open data applications. The latest version of Islandora, codenamed CLAW, integrates Fedora with Drupal 8, the popular content management system. CLAW takes full advantage of Fedora’s linked data capabilities while also leveraging Drupal’s powerful network of contributed modules to provide a modern, web-based repository platform that enables linked open data applications. This can be seen in production at the University of Toronto Scarborough (UTSC), where CLAW has been used to build a site that provides Palladio visualizations and exposes a SPARQL endpoint for complex RDF queries. This presentation will provide an overview of the latest versions of Fedora and Islandora CLAW with a focus on the linked data and web-based features and functionality. It will also use the UTSC site as an example of how Fedora can power linked open data applications. (Source code: fcrepo4, CLAW)
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".