Approximate Query for Industrial Fault Knowledge Graph Based on Vector Index

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.