Comparing Heuristic Rules and Masked Language Models for Entity Alignment in the Literature Domain
Bibliographic record
Abstract
The cultural world offers a staggering amount of rich and varied metadata on cultural heritage, accumulated by governmental, academic, and commercial players. However, the variety of involved institutions means that the data are stored in as many complex and often incompatible models and standards, which limits its availability and explorability by the greater public. The adoption of Linked Open Data technologies allows a strong interlinking of these various databases as well as external connections with existing knowledge bases. However, as they often contain references to the same entities, the delicate issue of entity alignment becomes the central challenge, especially in the absence or scarcity of unique global identifiers. To tackle this issue, we explored two approaches, one based on a set of heuristic rules and one based on masked language models, or masked language models (MLMs). We compare these two approaches, as well as different variations of MLMs, including some models trained on a different language, and various levels of data cleaning and labeling. Our results show that heuristics are a solid approach but also that MLM-based entity alignment obtains better performance coupled with the fact that it is robust to the data format and does not require any form of data preprocessing, which was not the case of the heuristic approach in our experiments.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".