Comparing Information Structures Used in the Maritime Defence and Security Domain
Bibliographic record
Abstract
As assets are engaged to collect data and intelligence on the entities or parties of interest to a defense or security operation, much of the collected data are represented in electronic form and subsequently stored in an assortment of files or databases. It is also typical that these data or information need to be shared with others involved in the investigation, task group, coalition, or security force. The effectiveness of this sharing is ultimately judged by how well the receiving party understands the information it has received. In this paper we examine the commonality of concepts between information systems in the Canadian defense and security regime. Specifically, we consider components of the National Information Exchange Model (NIEM), the Canadian Naval Positioning Repository (NPR), a port clearance structure, and a messaging structure that evolved out of a Canadian defense research and development effort. We then investigate the performance of graph-based approaches for automatic schema matching over these schemas. The information structures, or schemas, are examined using the open-source software for combining match algorithms (COMA). Results of the investigation show how the diverse terminology in maritime defense and security introduces unnecessary differences in the vocabularies (e.g., using vessel, ship, or identity as the descriptor for an object). Results also show how discrepancies are introduced through data typing, the structure itself, and semantics. Overall, the investigation indicates that the graph-based methods do not appear to offer a way of automating structure matching while maintaining confidence in the output for schema used across the different systems underlying the Canadian MDA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.011 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".