Bibliographic record
Abstract
In our previous work [1], to improve search quality and user satisfaction by using the user's context of search we have developed a FOCS Model. In this Model, a context ontology is developed to record user's relevant context information and to help the semantic expansion of keywords. To make search results more relevant and personalized, similarity flooding algorithm is used to match context ontology with a faceted ontology, an ontology for annotating the target documents. However, similarity flooding algorithm cannot be used to calculate semantic data, so it is not a desirable method to solve the problem of context search well. Hence, we implemented a new Context Search in the model of IORCS. In this model, we integrate an inconsistent ontology reasoning method into our context search model to improve the accuracy of ontology matching step. First, we consider the ontologies we use in context search model as inconsistent ontologies. Second, to filter sub-ontology which the most similar to Search Ontology from related faceted ontologies, context reasoning function (CRF) is introduced to filter these inconsistent ontologies. Finally, context reasoning algorithm is used to implement ontology matching. The experimental results show the integration of inconsistent ontology reasoning into context search model can tackle the issue of Semantic Similarity Calculation better.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".