An Exploration of IFLA LRM for Literature Data Representation
Bibliographic record
Abstract
The digital humanities have witnessed a clear development in recent years due partly to their adoption of Semantic Web and linked data technologies and the creation of knowledge bases. In this work, we target the creation of an ontology and knowledge base for literature data representation based on the IFLA Library Reference Model (LRM). IFLA LRM is the main model for book-related data, allowing for a fine representation of the various layers that constitute a book. However, by design, it doesn’t deal with some aspects usually available in literature databases, such as information about authors, literary awards or book themes. As a result, LRM requires some extensions to be able to represent ancillary data. Another challenge is the querying of IFLA LRM knowledge bases, with a performance cost that comes with the fine-grained expressivity of the LRM model, which creates longer and therefore typically slower SPARQL queries. In this work, we propose an extension to the IFLA LRM ontology called IFLA LRM* that targets these limitations including a connection to the vocabulary Schema.org and to the taxonomies Thema and Dewey Decimal, and the representation of literary awards. We also present a practical case study on using our extended model to create a Quebec literature knowledge base, discussing the interest of our extensions.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".