Ontologies and Research Data
Bibliographic record
Abstract
This study examines the relationship between ontologies and open research data within the framework of the Semantic Web. The objective is to investigate the interconnectivity of both subjects through scholarly works that offer enhancements to the Web ecosystem and scientific research processes. The paper reviews critical theoretical frameworks related to the Semantic Web and the significance of metadata within this model. It also delves into the function that ontologies can fulfill in the Semantic Web landscape. The paper provides a historical overview of the emergence of ontologies and definitions in both Computer Science and Information Science. It chronicles how theorists in Information Science have progressively embraced the concept of ontologies since the late 20th century and assesses the current scholarly consensus on the subject. The study also addresses the importance of open research data in modern science by doing a systematic literature review; this study sources relevant publications from the Web of Science and Scopus databases, with a temporal focus from 2000 to 2023. The findings offer a comprehensive analysis of existing literature that bridges the two domains above, aiding in the theoretical and methodological systematization of the subject matter. The discussion section elaborates on the findings, offering insights into the evolutionary trajectory of the subject matter. Emphasis is placed on the utility of ontologies as tools for the sustainable and effective utilization of research data, accentuating the value of such data as a basis for future scholarly work. In conclusion, we advocate for information science to take a leading role in initiatives that leverage ontological frameworks to manage specialized knowledge in research data sets effectively, ensuring that such data remains an asset for advancing scientific understanding.
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 | 0.039 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.034 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".