Semantic Frameworks to Enhance Situation Awareness for Defence and Security Applications
Bibliographic record
Abstract
This research project outlines some methods and tools developed in order to integrate knowledge into various processing chains implemented for defence and security applications. Project contributions focused on several aspects. First, assessing the quality of information provided by humans, for which two main approaches were developed relying either on the semantics of ontologies to detect inconsistency and contradictions or on the properties of sources in the reporting chain to assign a degree of trust to information items. Second, the use of semantics as a backbone for information retrieval, reuse of experiences, and data fusion in heterogeneous and dynamic environments has been investigated.Those approaches contribute to the overcoming problem of dynamically integrating resources, user feed-back, sensor data and observations, which is a critical aspect of situation awareness today. Third, the exploitation of open sources and social data in the context of defence and security applications has been addressed. Those methods have been developed in collaboration with several fellow researchers and include contributions from master thesis and post docs. The work was carried out in the framework of several research projects, with funding from the European Commission, ANR and CNRS. The projects involved academic and industrial partners, such as: George Mason University (USA), DRDC (Canada), FKIE (Germany), NATO CMRE (Italy), University of Paris Nanterre, University of Toulouse II (France), Thales TNO (Netherlands) and Airbus D&S (France).
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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".