Mining actionable concepts in concept lattice using Interestingness Propagation
Bibliographic record
Abstract
Mining important conceptual patterns is an essential task for understanding the context and content of complex data in many scientific and engineering applications . While exact relevance indices in Formal Concept Analysis provide accurate importance evaluation that can be used for extracting interesting concepts, they often have expensive algorithmic complexity (e.g., at least quadratic in the lattice size, which is exponential with respect to the context size). This frequently results in a computational bottleneck, rendering these indices inappropriate for large, dense contexts or small contexts with large concepts. In this paper, we introduce I nterestingness P ropagation ( IP ), an efficient message passing strategy for identifying actionable concepts based on interesting and uninteresting information available from a very small portion of the concepts in lattices. From a conceptual perspective, IP leverages the lattice local and global conceptual structures to maintain local and global consistency in the interesting or uninteresting labeling information among similar neighborhood concepts. Experiments on synthetic and real-world datasets show that IP can accurately extract interesting concepts that are very competitive with ground truth ones computed using state-of-the-art exact interestingness indices, while being at least three times faster than certain indices like CR and one order of magnitude faster than others like stability.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".