Decentralised Knowledge Graph Evolution via Blockchain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, knowledge graphs (KGs) have been applied in various domains, where the construction and maintenance of the KGs are usually time- and labor-intensive. In this context, constructing shareable KG through multiple constructors is being attempted to reduce costs. In this collaborative process, security and quality issues are critical. The system for constructing shareable KGs should be capable to recover the KG from most malicious attack and to filter out wrong triples from dynamically submitted ones. Blockchain could naturally prevent malicious tampering with its record data, perfect for solving the security issue. However, the integration of multi-source KGs as well as the quality issue still lacks solutions. To address the issues, this paper proposes a blockchain-based high-quality KG collaborative construction framework to ensure the KG quality in its long-term evolution. The framework is built on the underlying consensus mechanism of the blockchain, adopted to an extensible data structure to store multi-source triples on the distributed ledger. A smart contract is implemented to publish triples, assess the contributor credibility and evaluate triple quality to keep the KG in high-quality. Anti-attack mechanisms are designed to defend against malicious triple submissions. Experiments are conducted demonstrating the effectiveness of the framework.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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