Ontology alignment using relative entropy for semantic uncertainty analysis
Bibliographic record
Abstract
The development and use of many diverse ontologies to support the representational needs of different sources and different contexts is common and necessary. However, the increased sharing of databases implementing heterogeneous ontologies pose the problem of ontological alignment. Ontology alignment typically consists of manual operations from users with different experiences and understandings and limited reporting is conducted in the quality of mappings. To assist the International Organization for Standards (ISO) in standards development for information and data quality assessment, we propose an approach using relative entropy for semantic uncertainty analysis. Information theory has widely been adopted and provides uncertainty assessment for quality of service (QOS) analysis. Quality of information (QOI) or Information Quality (IQ) definitions for semantic assessment can be used to bridge the gap between ontology (semantic) uncertainty alignment and information theory (symbolic) analysis. Pragmatically aiding users of the shared ontologies requires assessments of the cognitive mental models, recognition of semantic classifications, and action over timeliness, throughput, confidence, and accuracy of the translations. In this paper, we explore issues of ontology uncertainty alignment utilizing the elements of information theory (KL divergence or relative entropy). A maritime domain situational awareness example with ship semantic labels is shown to demonstrate ontology alignment uncertainty assessment for data quality standards to assist users for pragmatic surveillance.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".