S+EPP: Construct and Explore Bisimulation Summaries, plus Optimize Navigational Queries; all on Existing SPARQL Systems
Why this work is in the frame
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Bibliographic record
Abstract
We demonstrate S+EPPs, a system that provides fast con-struction of bisimulation summaries using graph analyticsplatforms, and then enhances existing SPARQL engines tosupport summary-based exploration and navigational queryoptimization. The construction component adds a novel op-timization to a parallel bisimulation algorithm implementedon a multi-core graph processing framework. We show thatfor several large, disk resident, real world graphs, full sum-mary construction can be completed in roughly the sametime as the data load. The query translation componentsupports Extended Property Paths (EPPs), an enhance-ment of SPARQL 1.1 property paths that can express asignificantly larger class of navigational queries. EPPs areimplemented via rewritings into a widely used SPARQLsubset. The optimization component can (transparently tousers) translate EPPs defined on instance graphs into EPPsthat take advantage of bisimulation summaries. S+EPPscombines the query and optimization translations to enablesummary-based optimization of graph traversal queries ontop of off-the-shelf SPARQL processors. The demonstra-tion showcases the construction of bisimulation summariesof graphs (ranging from millions to billions of edges), to-gether with the exploration benefits and the navigationalquery speedups obtained by leveraging summaries storedalongside the original datasets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it