Multi-source semantic integration - revisiting the theory of signs and ontology alignment principles
Bibliographic record
Abstract
Current conceptual models for information fusion, including the JDL model do not consider the fact that their information sources are often based on different ontological bases. We therefore suggest that the Alignment functional process of the JDL model, which caters for space and time common referencing, be augmented with the notional aspect of common ontological alignment We illustrate this with the semiotic triangle and show how symbols relate to conceptualizations which in turn refer to objects of interest within domains of discourse. We then discuss on the notion of ontology alignment and how it is substantiated in a perspective of communication between a source (an addresser) and a fusion node (an addressee. Finally, we discuss on the semantic distance as a result of aligning similar (but not congruent) concepts.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.039 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".