Bibliographic record
Abstract
Unlike traditional database queries, keyword queries do not adhere to predefined syntax and are often dirty with irrelevant words from natural languages. This makes accurate and efficient keyword query processing over databases a very challenging task. In this paper, we introduce the problem of query cleaning for keyword search queries in a database context and propose a set of effective and efficient solutions. Query cleaning involves semantic linkage and spelling corrections of database relevant query words, followed by segmentation of nearby query words such that each segment corresponds to a high quality data term. We define a quality metric of a keyword query, and propose a number of algorithms for cleaning keyword queries optimally. It is demonstrated that the basic optimal query cleaning problem can be solved using a dynamic programming algorithm. We further extend the basic algorithm to address incremental query cleaning and top- k optimal query cleaning. The incremental query cleaning is efficient and memory-bounded, hence is ideal for scenarios in which the keywords are streamed. The top- k query cleaning algorithm is guaranteed to return the best k cleaned keyword queries in ranked order. Extensive experiments are conducted on three real-life data sets, and the results confirm the effectiveness and efficiency of the proposed solutions.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".