Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping
This work addresses scalability and adoption issues in industrial analytics for companies like Bosch, though it appears incremental as it builds on existing ontology and KG methods.
The paper tackles the problem of poor quality knowledge graphs in industrial analytics due to mismatched ontologies, proposing an ontology reshaping approach that improves query writing time and reduces storage redundancy, as evaluated with real-world Bosch data.
Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a convenient and standardised way to exchange data and to layer analytical applications over it. However, poor design of ontologies of high degree of mismatch between them and industrial data naturally lead to KGs of low quality that impede the adoption and scalability of industrial analytics. Indeed, such KGs substantially increase the training time of writing queries for users, consume high volume of storage for redundant information, and are hard to maintain and update. To address this problem we propose an ontology reshaping approach to transform ontologies into KG schemata that better reflect the underlying data and thus help to construct better KGs. In this poster we present a preliminary discussion of our on-going research, evaluate our approach with a rich set of SPARQL queries on real-world industry data at Bosch and discuss our findings.