Indexing mixed types for approximate retrieval
Bibliographic record
Abstract
In various applications such as data cleansing, being able to retrieve categorical or numerical attributes based on notions of approximate match (e.g., edit distance, numerical distance) is of profound importance. Commonly, approximate match predicates are specified on combinations of attributes in conjunction. Existing database techniques for approximate retrieval, however, limit their applicability to single attribute retrieval through B-trees and their variants. In this paper, we propose a methodology that utilizes known multidimensional indexing structures for the problem of approximate multi-attribute retrieval. Our method enables indexing of a collection of string and/or numeric attributes to facilitate approximate retrieval using edit distance as an approximate match predicate for strings and numeric distance for numeric attributes. The approach presented is based on representing sets of strings at higher levels of the index structure as tries suitably compressed in a way that reasoning about edit distance between a query string and a compressed trie at index nodes is still feasible. We propose and evaluate various techniques to generate the compressed trie representation and fully specify our indexing methodology. Our experimental results show the benefits of our proposal when compared with various alternate strategies for the same problem.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".