A Hybrid Semantic Networks Construction Framework for Engineering Design
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper proposes a novel framework for building semantic networks from a seed design statement using Recursive Object Modeling (ROM), Word2Vec language modeling, and vector semantic-based method. Semantic Scholar API was used to retrieve abstracts of scientific papers to build ROM-based Semantic Networks to address the design problem implied in the seed design statement, following Environment Analysis from Environment-Based Design (EBD) methodology. The proposed framework was applied to construct the semantic network for a project to design aircraft braking systems, which demonstrates the framework's efficiency. The presented research makes two major contributions: a ROM-based phrase extractor and a domain-specific language model, which is trained on the automatically collected literature abstracts. Using a manually created and assessed truth set containing 100 pairs of abstract-key phrases, the phrase extractor was evaluated by benchmarking it with two existing off-the-shelf key phrase extraction algorithms: TextRank and Rake. The ROM-based phrase extractor extracted most key phrases from target domains and showed higher precision, recall, and F-1 scores than other methods. Meanwhile, the trained project-specific language model was evaluated using the NASA thesaurus. We randomly sampled 457 pairs of connected domain-specific terms related to aircraft braking and landing knowledge. Our Skip-gram model was compared with Google's pre-trained word2vec model and a baseline word2vec model. The results demonstrated that our language model could detect the most pairs of concepts from the NASA thesaurus. The generated semantic network can be applied to design information retrieval, computer-aided design idea generation, cross-domain communication support system, and designer training tool.
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.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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