The contribution of information science in the Semantic Web research landscape
Bibliographic record
Abstract
ABSTRACT Semantic Web and Linked Data promise to change the way we identify, classify, share, find and reuse information by managing and interlinking data so that it can be understood by machines. The benefits identified by the World Wide Web Consortium and other Semantic Web advocates have lead information professionals to explore these possibilities by implementing several initiatives based on Semantic Web technologies in recent years. Just like information science, Semantic Web research is multidisciplinary in nature and can be applied in many different disciplines. In this article, we provide a portrait of research on the Semantic Web using bibliometric methods and investigate the specific contribution of information science literature on the topic. We analyzed 6,438 articles published from 2001 to 2016 retrieved from the Web of Science database to evaluate the evolution of the papers published by discipline and the way the topic was discussed by different discipline. A citation analysis is also conducted.
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 | 0.047 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.073 | 0.082 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".