Bibliographic record
Abstract
It is our great pleasure to welcome you to the 6th International Workshop on Usage Analysis and the Web of Data (USEWOD), associated with WWW 2016.The workshop is dedicated to the diverse ecosystem of Web of Data access mechanisms. From academic to government data, from complex SPARQL queries to Linked Data Fragments, from DBpedia to Wikidata: mthe data sources on the Web of Data and the ways in which these sources can be created and consumed vary greatly and raise fundamental questions.The call for papers attracted submissions from United States, Canada, Asia, and Europe. The program committee reviewed and accepted the following: Venue or Track Reviewed -4 Accepted - 2.Additionally, we decided to publish an invited paper that presents a particularly interesting crossdisciplinary perspective on the Web of Data and outline current research directions and challenges in an extended 'Message from the USEWOD Chairs'.The USEWOD 2016 Research Dataset As in previous years, a standard research dataset of usage data from well-recognized Web-of-Data datasets has been published to promote reproducible research on the workshop themes. A particular highlight of this year's dataset is overlapping usage data from the official DBpedia servers as well as the Linked Data Fragments interface to DBpedia and Wikidata. This dataset allows researchers to study alternative Web of Data usage mechanisms in an unprecedented way and could therefore become a unique resource of great importance for the field. For more information on the datasets released in previous years, please see http://usewod.org/data-sets.html. Special thanks got to Open Link Software for providing us with DBpedia logs as well as Ruben Verborgh for access to Linked Data Fragments usage data.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.735 | 0.662 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".