Bibliographic record
Abstract
We study the problem of domain search where a domain is a set of distinct values from an unspecified universe. We use Jaccard set containment score, defined as | Q ∩ X |/| Q |, as the measure of relevance of a domain X to a query domain Q . Our choice of Jaccard set containment over Jaccard similarity as a measure of relevance makes our work particularly suitable for searching Open Data and data on the web, as Jaccard similarity is known to have poor performance over sets with large differences in their domain sizes. We demonstrate that the domains found in several real-life Open Data and web data repositories show a power-law distribution over their domain sizes. We present a new index structure, Locality Sensitive Hashing (LSH) Ensemble, that solves the domain search problem using set containment at Internet scale. Our index structure and search algorithm cope with the data volume and skew by means of data sketches using Minwise Hashing and domain partitioning. Our index structure does not assume a prescribed set of data values. We construct a cost model that describes the accuracy of LSH Ensemble with any given partitioning. This allows us to formulate the data partitioning for LSH Ensemble as an optimization problem. We prove that there exists an optimal partitioning for any data distribution. Furthermore, for datasets following a power-law distribution, as observed in Open Data and Web data corpora, we show that the optimal partitioning can be approximated using equi-depth, making it particularly efficient to use in practice. We evaluate our algorithm using real data (Canadian Open Data and WDC Web Tables) containing up over 262 million domains. The experiments demonstrate that our index consistently outperforms other leading alternatives in accuracy and performance. The improvements are most dramatic for data with large skew in the domain sizes. Even at 262 million domains, our index sustains query performance with under 3 seconds response time.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".