Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We demonstrate S+EPPs, a system that provides fast construction of bisimulation summaries using graph analytics platforms, and then enhances existing SPARQL engines to support summary-based exploration and navigational query optimization. The construction component adds a novel optimization to a parallel bisimulation algorithm implemented on a multi-core graph processing framework. We show that for several large, disk resident, real world graphs, full summary construction can be completed in roughly the same time as the data load. The query translation component supports Extended Property Paths (EPPs), an enhancement of SPARQL 1.1 property paths that can express a significantly larger class of navigational queries. EPPs are implemented via rewritings into a widely used SPARQL subset. The optimization component can (transparently to users) translate EPPs defined on instance graphs into EPPs that take advantage of bisimulation summaries. S+EPPs combines the query and optimization translations to enable summary-based optimization of graph traversal queries on top of off-the-shelf SPARQL processors. The demonstration showcases the construction of bisimulation summaries of graphs (ranging from millions to billions of edges), together with the exploration benefits and the navigational query speedups obtained by leveraging summaries stored alongside the original datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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