Exploratory Search in Databases and the Web.
Bibliographic record
Abstract
The traditional way a user interacts with a database system is through queries. Structured query languages, such as SQL for relational data, XQuery for XML, and SPARQL for RDF data, allow users to submit queries that may precisely capture their information needs, but users need to be familiar with the underlying ontology and data structure and of course the query language itself. Moreover, users need to some extent be familiar with the content of the database and have a clear understanding of their information needs. These requirements stand as the weaknesses of this interaction mode. As data stored in databases grows in unprecedented rates and becomes accessible to diverse and less technically oriented audience, new forms of data exploration and interaction become increasingly more attractive. The World Wide Web represents the largest and arguably the most complex repository of content. Users seek information on the web through two predominant modes: by browsing or by searching. In the first mode, the interaction between the user and the data repository is driven directly by the user’s interpretation of their information need and their information foraging constraints. In the latter mode, a search engine typically mediates the user-data interactions and the process starts with the user entering query-terms that act as surrogates for the user information goals. Freetext queries allow end-users a simple way to express their information needs independently from the underlying data model and structure, as well as from a specific query language. Given a query, the most common strategy has been to present the results as a ranked list. Users have to subsequently peruse the list to satisfy their information needs through browsing the links and/or by issuing further queries. However, the information in the web gets rapidly diversified both in terms of its complexity as well as in terms of the media through which the information is encoded, spanning from large amounts of unstructured and semi-structured data to semantically rich available knowledge. Increasing de-
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".