Triple Extraction with Generative Technique for Constructing Weighted Knowledge Graph
Bibliographic record
Abstract
Extracting relational facts from unstructured text is crucial in natural language processing used in many applications, particularly in constructing knowledge graphs. Relational facts are represented as triples in which two entities are connected through a relation. This work introduces a new and effective end-to-end method to generate triples from the input text. In the proposed method, we develop an encoder-decoder-based transformer model and warm-start both the encoder and decoder with pretrained checkpoints that are publicly accessible. These checkpoints can be taken from models such as BERT, GPT-2, and RoBERTa. Experimental results show that our method achieves better results for triple extraction on publicly available datasets (NYT and WebNLG) than the other state-of-the-art techniques. Further, the extracted triples are processed and used to build a knowledge graph. Complete control of this process allows for determining the weights of the relations (triples). The weights reflect the frequency of occurrences of facts represented by the relations and provide the degree of confidence in the facts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".