A Formal Knowledge Retrieval System for Cognitive Computers and Cognitive Robotics
Bibliographic record
Abstract
Intelligent knowledge base theories and technologies are fundamentally centric in machine learning and cognitive robotics. This paper presents the design of a formal knowledge retrieval system (FKTS) for intelligent knowledge base modeling and manipulations based on concept algebra. In order to rigorously design and implement FKTS, real-time process algebra (RTPA) is adopted to formally describe the architectures and behaviors of FKTS. The architectural model of FKTS in the form of a set of unified structure models (USMs) is rigorously described. On the basis of USMs, functional models of FKTS are hierarchically refined by a set of unified process models (UPMs). The UPMs of FFTS are divided into two subsystems known as those of the knowledge visualization and knowledge base retrieval subsystems where the content-addressed searching mechanism is implemented in knowledge bases manipulations. The FKTS system is design and implemented as a part of the cognitive learning engine (CLE) for cognitive computers and cognitive robots.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".