Classifying the LOD cloud
Bibliographic record
Abstract
Massive amounts of data from different contexts and producers are collected and connected relying often solely on statistical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project “Digging Into the Knowledge Graph,” funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminology employed within the LOD cloud with terminology employed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in computer science, and the KO community, with members from linguistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".