Using Ontology Based Knowledge Discovery in Location Based Services
Bibliographic record
Abstract
Rapid development of information technology for mobile computing, improvement in the accuracy of positioning systems, and ubiquitous use of mobile devices has generated large quantities of raw trajectories that represent the movement of moving objects. Mining such data, which contain not only space and time attributes, but also context attributes, is significant for many applications within the location based services domain. Such systems can provide more effective services to users through an understanding of a moving objects location, its context, and interests. But, in spite of the fact that most service providers offer various services to users, they are unable to identify relevant customers at the right time and right place. Recently different methods of data mining have been used in location based services for extracting patterns and modeling the behavior of users. However, due to the excessive number of extracted patterns it has been very difficult to infer knowledge from these patterns for an application domain. Given that several criteria such as geographic area, the nature of the entity, etc., influence movement behavior, one needs to consider this complexly during the knowledge discovery process. Therefore, this paper proposes a model that integrates an ontology-based approach for efficient interpretation of extracted patterns from an objects movement behavior.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".