Grailog KS Viz: A Grailog Visualizer for Datalog RuleML Using an XSLT Translator to SVG
Bibliographic record
Abstract
Grailog embodies a systematics to visualize knowledge sources by graphical elements. Its main benefit is that the resulting visual presentations are easier to read for humans than the original symbolic source code. In this paper we introduce a methodology to handle the mapping from Datalog RuleML, serialized in XML, to an SVG representation of Grailog, also serialized in XML, via eXtensible Stylesheet Language Transformations (XSLT) 2.0/XML; the SVG is then rendered visually by modern Web browsers. This initial mapping is realized to target Grailog's "fully node copied" normal form. Elements can thus be translated one at a time, separating the fundamental Datalog-to-SVG translation concern from the concern of merging node copies for optimal (hyper)graph layout and avoiding its high computational complexity in this online tool. The resulting open source Grailog Knowledge-Source Visualizer (Grailog KS Viz) supports Datalog RuleML with positional relations of arity n>1. The on-the-fly transformation was shown to run on all recent major Web browsers and should be easy to understand, use, and extend.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".