Translation of Place Names Based on Knowledge Graph
Bibliographic record
Abstract
Place names are important carriers of spatial information and attribute information for geographic entities. Transliteration of place names refers to the use of Chinese characters to translate place names in another language. However, current place name transliteration work suffers from issues such as time-consuming and inconsistent manual translation, as well as low accuracy in machine translation. Therefore, this article proposes a knowledge graph based method for English place name transliteration. This method mainly solves the core problem in place name translation: syllable optimization. By using a deep learning based phoneme generation method to generate place name proper phonemes, it is convenient to optimize the syllables of proper names using the constructed English place name knowledge graph. This knowledge graph-based syllable optimization effectively solves problems in machine translation, such as incomplete rule representation, infinite loops, one-to-many optimization results, weight setting, and nested rule optimization. Experimental comparisons conducted on Canadian place names demonstrate that the accuracy of the transliteration based on the knowledge graph can reach 91.2%, indicating that the proposed method improves the accuracy and efficiency of place name translation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".