Tensor-Based Knowledge Fusion and Reasoning for Cyberphysical-Social Systems: Theory and Framework
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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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".