Analogical stimuli retrieval approach based on R-SBF ontology model
Bibliographic record
Abstract
Analogy-based design is an effective approach for innovative design involving knowledge representation, analogical sources retrieval, mapping between two systems, and adaptation of candidate solutions. The knowledge representation is the first task to determine whether analogy can be implemented successfully. It is, therefore, necessary to have an efficient description method for knowledge representation and information retrieval. Based on the Structure-Behaviour-Function (SBF) model, this paper proposes an R-SBF model to integrate the flow, structure, behaviour and function of design knowledge. Relational types of states are used to describe the information behaviour with relations of ‘and’, ‘or’, ‘not’, ‘sequence’, ‘parallel’ and ‘feedback’. The R-SBF ontology model is constructed using the ontology editing tool Protégé 5.2.0 to link the knowledge representation and analogues retrieval. In the proposed algorithm of computing function similarity, both the semantic similarity and conceptual correlation are considered for the query scalability. The evaluation criteria for feasibility include Recall, Precision, and F-measure. An analogy-aided design innovation software prototype is developed to support the design activity. The improvement of a robot vacuum cleaner is discussed as an example of applications of the proposed method. The solution illustrates the effectiveness of the analogical stimuli to facilitate the design performance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".