S+EPP: Construct and Explore Bisimulation Summaries, plus Optimize Navigational Queries; all on Existing SPARQL Systems
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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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".