TOWARDS A COLLABORATIVE KNOWLEDGE DISCOVERY SYSTEM FOR ENRICHING SEMANTIC INFORMATION ABOUT RISKS OF GEOSPATIAL DATA
Bibliographic record
Abstract
Abstract. The aim of this research is to design and implement a knowledge discovery system that facilitates, using a web 2.0 collaborative approach, the identification of new risks of geospatial data misuse based on a contributed knowledge repository fed by application domain experts. [Context/Motivation] This research is motivated by the irregularity of risk analysis efforts and the poor semantic of the collected information about risks. In the context of risk analysis during geospatial database design, the knowledge about risks of geospatial data misuse is typically held by domain application experts. The collection and record of that knowledge are usually considered as optional activities. It is usually performed through face-to-face risk assessment meetings and reports. Such techniques end up by restricting the scope of risk analysis to a set of obvious risks usually already identified. Besides, little consideration is devoted to the storage of risk information in an appropriate format for automatic reasoning and new risk information discovery. As a consequence, many foreseeable risky aspects inherent to the data remain overlooked leading to ill-defined specification and faulty decisions. [Principal ideas/results] In this paper, we present a contributed knowledge discovery system that aims at enriching the semantic information about risks of geospatial data misuse in order to identify foreseeable risks. The proposed web-based system relies on a systematic and more active involvement of users in risk analysis. The approach consists of 1) providing an overview of the related work in the domains of risk analysis within the context of geospatial database design, 2) presenting an ontology-based knowledge discovery system that helps experts in risks identification based on an upper-level risk ontology and on a structured representation of the domain-specific knowledge and, 3) presenting the components of the proposed system architecture and how it may be implemented and used in practice, and finally 4) we conclude by discussing the approach. [Contribution] A major outcome is that the proposed platform can help discovering implicit domain knowledge, and facilitating the identification of foreseeable risks of geospatial data misuse in a way to preventively improve the resulting fitness-for-use.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".