Tensor-Based Knowledge Fusion and Reasoning for Cyberphysical-Social Systems: Theory and Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cyberphysical-social systems (CPSS) integrate human, machine, and information into large-scale automated systems and generate complex heterogeneous big data from multiple sources. Knowledge graphs play a pivotal role in energizing the data with huge volume and uneven quality to drive CPSS intelligent applications and services, thus attracting intense research interests from scholars. The Resource Description Framework (RDF) describes knowledge in the form of subject-predicate-object triples and interpreted as directed labeled graphs. However, the graph structure doesn’t have flexible operability and direct computability in the theoretical framework, although it can be understood intuitively. Therefore, we proposed a tensor-based knowledge analysis framework in this article, which supports the representation, fusion, and reasoning of knowledge graphs. First, we employ Boolean tensors to represent heterogeneous knowledge graphs completely. Then, we present a series of graph tensor operations for the modification, extraction, and aggregation of high-order knowledge graphs. Furthermore, we perform tensor 1-mode product operation between the knowledge graph representation tensor and the entity representation tensor to obtain the relation path tensor, so as to infer the relationship between any two entities. Finally, we demonstrate the practicality and effectiveness of the proposed model by implementing a case study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it