Approximate Query for Industrial Fault Knowledge Graph Based on Vector Index
Bibliographic record
Abstract
In the industrial sector, index-based fault knowledge graph query techniques are essential for accelerating fault information retrieval and improving the accuracy and efficiency of diagnosing equipment issues. Using knowledge graph embedding, these systems transform entities and their relationships into dense vectors, making it easier for machine learning algorithms to process knowledge graph queries effectively. However, existing models often focus on boosting search accuracy at the cost of time efficiency, particularly when dealing with large fault knowledge graphs. To address this, we propose an optimized query method for fault knowledge graphs using vector indexing. The process starts by converting the entities and relationships in the knowledge graph into a vector space, generating a concise vector representation. Advanced vector database technology is then employed to build a specialized vector index library designed for fault knowledge graphs. This includes dividing the search space through clustering algorithms and employing approximate matching techniques to enhance query speed. By utilizing the indexed fault knowledge graph, we can conduct similarity searches to facilitate approximate querying. Evaluations show that our approach significantly reduces search times and outperforms traditional methods in terms of accuracy, demonstrating the value of vector index libraries in boosting the overall query efficiency of knowledge graphs, while keeping high accuracy levels.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.004 | 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".