Efficient keyword proximity search using a frontier-reduce strategy based on<i>d</i>-distance graph index
Bibliographic record
Abstract
Current keyword proximity search approaches on general graph lack effective means to reduce the search space, and thus suffer from low efficiency when dealing with large search space. In this paper, we present a novel approach in order to address this problem. Our approach employs a best-effort frontier-reduce strategy that aims to find a set of subgraphs containing the best answers. So we need only to search over these small subgraphs to get the top-k answers, and thus the efficiency can be significantly improved. To fulfill our strategy, we define a d-distance subgraph with upper size bound, and extract such subgraphs from the graph to build a new index structure combining the mappings between keywords, vertexes and subgraphs, by which we can quickly look up the target subgraphs for specific queries. Then, we perform an efficient algorithm to find the top-k answers, which can overcome the subgraph overlap problem and support existing optimal prioritization techniques.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".