Bibliographic record
Abstract
Our daily life is increasingly influenced by data-driven decision processes, both for good (e.g., machine learning algorithms being used to detect cancer ) and bad purposes (e.g., Cambridge Analytica influencing the U.S. presidential elections ).These decision processes rely on a large amount of (input) data.To create high-quality decision processes, a large amount of high-quality data is needed.However, it is infeasible to integrate large amounts of diverse data manually.This is one of the reasons that a set of technologies, namely, Semantic Web technologies, are proposed.Semantic Web technologies allow generating and processing so-called knowledge graphs: a means to exchange data values, and additionally their meaningful (i.e., semantic) relationships, between multiple agents.These agents generate and use data in different ways on a large scale.Knowledge graphs thus ease (automatic) data integration, and can thus be an essential element of future data-driven decision processes.However, automatic processing (of knowledge graphs) lacks manual inspection (i.e., human supervision that can help to interpret and nuance results), and thus requires a strong level of quality assessment.Without quality assessment, the past has shown that data can be incorrectly integrated due to semantic differences (e.g., mixing the metric and imperial system, which led to the crash of a NASA Mars orbiter ) or processed badly (e.g., introducing rounding errors, halving the value of the Vancouver Stock Exchange in less than a year ).If we want to use knowledge graphs in a real-world context that does not result in crashes or massive money loss, we need to improve and assess its quality, both on the level of data values and semantic relationships.This dissertation focuses exactly on this: improving data quality and assessing semantic quality of knowledge graphs.We specifically investigate following two challenges that complementary tackle knowledge graph quality.The first challenge is including data transformations in knowledge graphs as this can help cleaning the data (e.g., by including data nor-K.Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis, "Machine learning applications in cancer prognosis and prediction," Computational and Structural Biotechnology Journal, vol., pp. -, .D. Byers, "Facebook is facing an existential crisis," CNN Business, Mar.th .A. G. Stephenson et al., "Mars Climate Orbiter Mishap Investigation Board Phase I Report" NASA, .K. Quinn, "Ever Had Problems Rounding Off Figures?This Stock Exchange Has," The Wall Street Journal, p. , Nov. th .
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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 | 0.039 | 0.263 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".