A framework for semantic link discovery over relational data
Bibliographic record
Abstract
Discovering links between different data items in a single data source or across different data sources is a challenging problem faced by many information systems today. In particular, the recent Linking Open Data (LOD) community project has highlighted the paramount importance of establishing semantic links among web data sources. Currently, LOD sources provide billions of RDF triples, but only millions of links between data sources. Many of these data sources are published using tools that operate over relational data stored in a standard RDBMS. In this paper, we present a framework for discovery of semantic links from relational data. Our framework is based on declarative specification of linkage requirements by a user. We illustrate the use of our framework using several link discovery algorithms on a real world scenario. Our framework allows data publishers to easily find and publish high-quality links to other data sources, and therefore could significantly enhance the value of the data in the next generation of web.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".