Demonstration of SPARQL <sup> <i>ML</i> </sup> : An Interfacing Language for Supporting Graph Machine Learning for RDF Graphs
Bibliographic record
Abstract
This demo paper presents KGNet, a graph machine learning-enabled RDF engine. KGNet integrates graph machine learning (GML) models with existing RDF engines as query operators to support node classification and link prediction tasks. For easy integration, KGNet extends the SPARQL language with user-defined predicates to support the GML operators. We refer to this extension as SPARQL ML query. Our SPARQL ML query optimizer is in charge of optimizing the selection of the near-optimal GML models. The development of KGNet poses research opportunities in various areas spanning KG management. In the paper, we demonstrate the ease of integration between the RDF engines and GML models through the SPARQL ML inference query language. We present several real use cases of different GML tasks on real KGs. Using KGNet, users do not need to learn a new scripting language or have a deep understanding of GML methods. The audience will experience KGNet with different KGs and GML models, as shown in our demo video and Colab notebook.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".