Proceedings of the First International Workshop on Keyword Search on Structured Data
Bibliographic record
Abstract
Information search is an indispensable component of our lives. Web search engines are widely used for searching textual documents, images, and video. However, there are also vast collections of structured and semi-structured data both on the Web and in enterprises, such as relational databases, XML data, etc. Traditionally, to access these resources, a user must learn structured or semi-structured query languages, and must be able to access data schemas, which are most likely heterogeneous, complex, and fastevolving. To relieve web and scientific users from the learning curve and enable them to easily access structured and semi-structured data, there is a growing research interest to support keyword search on these data sources. The first International Workshop on Keyword Search on Structured Data (KEYS 2009) is held in Providence, Rhode Island, USA on 28th June, 2009, in conjunction with SIGMOD 2009 conference, and aims to encourage researchers from both academia and industry communities to discuss the opportunities and challenges in keyword search on (semi-)structured data, and to present the key issues and novel techniques in this area. In response to the call for papers, KEYS 2009 has attracted 25 submissions. The submissions are highly diversified, coming from Canada, China, Germany, Italy, Japan, Greece, Singapore, Sweden, Thailand, and USA, resulting in an international final program. All submissions were peer reviewed by three program committee members. The program committee selected 6 full research papers and 4 demo and poster papers for inclusion in the proceeding. The accepted papers covered a wide range of research topics and novel applications on keyword search on structured data.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.035 |
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".