Keyword Search over Dynamic Categorized Information
Bibliographic record
Abstract
Consider an information repository whose content is categorized. A data item (in the repository) can belong to multiple categories and new data is continuously added to the system. In this paper, we describe a system, CS*, which takes a keyword query and returns the relevant top-K categories. In contrast, traditional keyword search returns the top-K documents (i.e., data items) relevant to a user query. The need to dynamically categorize new data and also update the meta-data required for fast responses to user queries poses interesting challenges. The brute force approach of updating the meta-data by comparing each new data item with all the categories is impractical due to (i) the large cost involved in finding the categories associated with a data item and (ii) the high rate of arrival of new data items. We show that a sampling based approach which provides statistical guarantees on the reported results is also impracticable. We hence develop the CS* approach whose effectiveness results from its ability to focus on a strategically chosen subset of categories on the one hand and a subset of new data on the other. Given a query, CS* finds the top-K categories with high accuracy even in time-constrained situations. An experimental evaluation of the CS* system using real world data shows that it can easily achieve accuracy in excess of 90%, whereas other approaches demand at least 57% more resources (i.e., processing power), for providing similar results. Our experimental results also show that, contrary to expectations, if the rate of arrival of data items doubles, whereas CS* continues to provide high accuracy without a significant increase in resources, other approaches require more than double the number of resources.
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.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".