Latent Semantic Extraction and Analysis forTRIZ-Based Inventive Design
Bibliographic record
Abstract
During the development of TRIZ, several knowledge sources have been developed to solve inventive problems. Even though they are about close notions, the level of detail of the descriptions is very dissimilar, making it difficult for the user to operate with them in a systematic way. To cope with this difficulty, we are interested in finding semantic links among these sources, with the goal of assisting the inventive design expert during his activities. Taking into account that all the TRIZ knowledge sources are represented as short texts and directly applying conventional topic models on such short texts may not work well, an extended latent semantic analysis model is proposed to find the missing links among the TRIZ knowledge sources. With the help of these obtained links, several heuristic abstract solutions can be obtained. In order to show this whole process, the resolution of the case of the 'Auguste Piccard's Stratostat' is elaborated in detail. [Received: 11 March 2017; Revised: 21 November 2017; Accepted 30 March 2018]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".