Knowledge Graphs, Category Theory and Signatures
Bibliographic record
Abstract
Introduction of graph-based data representation formats, that resulted in Knowledge Graphs and Linked Open Data, enables new ways of processing and analyzing relations between individual pieces of data. One of the most important features of such representation is its ability to represent data semantics. We state that an important step towards obtaining a full utilization of graph-based semantics is to create a formal process of extracting underlying structures of data from Knowledge Graphs and Linked Open Data, as well as building data models. The paper proposes a methodology, based on category theory, for representing graph-based data as a topos category. Construction of topos give us the ability to identify two types of features: ones that are involved in definitions of other concepts; and ones that show how other concepts are involved in a definition of a given concept. Topos and structures of features allow for reasoning about concepts and their interrelations. Further, mechanisms of category theory enable to synthesize new concepts. A simple example is included.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".