Tools for Industrial Knowledge Modeling and Management
Bibliographic record
Abstract
This paper presents the design and implementation of software tools for modeling and managing knowledge to be used in knowledge based systems or the semantic Web. The tools presented have been developed based on the inferential modeling technique, which is a technique for modeling the static and dynamic knowledge elements of a problem domain. Some major deficiencies of existing tools include the lack of support for modeling dynamic knowledge and knowledge management. To address the inadequacies, the focus of this work is on knowledge management and dynamic knowledge modeling. To address the first objective of knowledge management, a Class Editor, which supports management of static knowledge, has been developed. The Class Editor supports knowledge sharing and re-use by automatically managing author information and access rights in a peer-to-peer environment. To address the second objective of modeling dynamic knowledge, a Protege plug-in, called Dyna, has been developed, which supports dynamic knowledge modeling. Task behaviour, which is a component of dynamic knowledge, is being modeled using Task Behaviour Language (TBL), and test cases for task behaviour can be created in TBL. Test cases are runnable, enabling verification that the model is working as expected. The dynamic knowledge models are stored in XML and can be shared and re-used. The suite of tools will be applied for constructing a knowledge model in the petroleum remediation selection domain.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".