Yaanii: Effective Keyword Search over Semantic Dataset.
Bibliographic record
Abstract
Nowadays data is disseminated in a number of different sources, from databases systems to the Web, from a traditional structured organization (relational) to a semi-structured (XML), up to the unstructured ones (text in Web documents). Although availability of data is constantly increasing, one principal difficulty users have to face is to find and retrieve the information they are looking for. To this aim keywords search based systems are increasingly capturing the attention of researchers. In this paper, we present Yaanii1, a tool for the effective Keyword Search over semantic datasets. It is based on a novel keyword search paradigm for graph-structured data, focusing in particular on the RDF data model. While many techniques search the best answer trees, we propose an effective algorithm for the exploration and computation of all matching subgraphs. We provide a clustering technique that identifies and groups graph substructures based on template match. A scoring function, IR inspired, evaluates the relevance of the substructures and the clusters. A strong point of our approach is that the ranking supports the generation of Top-k solutions during its execution.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".