Automatic Extraction of Causally Related Functions From Natural-Language Text for Biomimetic Design
Bibliographic record
Abstract
Identifying relevant analogies from biology is a significant challenge in biomimetic design. Our natural-language approach addresses this challenge by developing techniques to search biological information in natural-language format, such as books or papers. This paper presents the application of natural-language processing techniques, such as part-of-speech tags, typed-dependency parsing, and syntactic patterns, to automatically extract and categorize causally related functions from text with biological information. Causally related functions, which specify how one action is enabled by another action, are considered important for both knowledge representation used to model biological information and analogical transfer of biological information performed by designers. An extraction algorithm was developed and scored F-measures of 0.78–0.85 in an initial development test. Because this research approach uses inexpensive and domain-independent techniques, the extraction algorithm has the potential to automatically identify patterns of causally related functions from a large amount of text that contains either biological or design information.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".