Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping

TL;DR

This paper proposes an ontology reshaping method to improve knowledge graph quality for industrial analytics, enhancing data integration, query efficiency, and maintainability in industrial settings.

Contribution

It introduces an ontology reshaping approach that aligns ontologies with industrial data to create higher quality knowledge graphs for analytics.

Findings

Improved query performance on real-world Bosch data

Reduced storage redundancy in knowledge graphs

Enhanced maintainability of industrial knowledge graphs

Abstract

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.