Ontology alignment in geographical hard-soft information fusion systems
Bibliographic record
Abstract
Information fusion exists over many forms of hard data (e.g. from physical sensors) and soft data (e.g. from human reports) to interpret observations of real-world objects. As demonstrated from the Geographical Information Systems (GIS) community, there is a growing need for the linking and alignment of both (1) exploited physical imagery products and (2) derived ontological textual labels (semantic markup). Semantic markup can be done on both exploited data (e.g. automated image segmentation), as well as user reports (e.g. weather forecasts). Since the derived information is collected, stored, and displayed into distinct ontological structures by different agencies; ontological alignment is thus required whenever the semantic information is paired with distinct real-world imagery observations. In this paper, we explore issues of fusing hard and soft data as related to ontology alignment. A maritime domain situational awareness example with geographical imagery and textural ontologies is shown to demonstrate the need for ontology alignment 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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".