PhD - Explainable Knowledge Graph Completion for Intelligent Manufacturing Systems

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Job Description

Knowledge Graphs (KGs) have proven to be a key factor to foster the success of Smart Manufacturing. The data generated in these contexts need to be semantically harmonized and made available for a myriad of applications.
However, due to the huge level of heterogeneity, as well as the underlying data quality, there exist the requirement to continuously update and improve these KGs by means of knowledge graph completion (KGC) methods like Link Prediction or Node Classification. Besides, state-of-the-art KGC approaches fail to consider the quality constraints while generating predictions, resulting in the completion of KGs with erroneous relationships.

• To successfully adopt KGs and its enhancements made by KGC methods, a Neuro-Symbolic approach is required. The approach will enable manufacturing experts to understand and apply the output obtained by the methods and properly evaluate whether the results make sense according to their knowledge.
• This thesis will research innovative Neuro-Symbolic methods to improve KGC methods in smart manufacturing scenarios. Emphasis will be placed on the representation of heterogenous and relation-based knowledge and the combination with learning-based approaches to enable explainability and transparency of the predictions.
• The thesis will validate the methods on one or more real use cases at Bosch. Evaluations will be performed on public benchmarking challenges in smart manufacturing recognized by the community and internal Bosch datasets.

Qualifications

• Education: excellent master’s degree in the field of Computer Science, Mathematics, Engineering, or a related field (please provide your degree marks)
• Experience and Knowledge: strong experience in machine learning and preferable in graph embedding and graph neural networks, knowledge in deep learning frameworks (PyTorch); good programming skills in Python and presentation skills as well as basic knowledge of semantic methods and ontologies
• Personality and Working Practice: you can approach others openly, communicate your ideas clearly and actively seek out new challenges
• Languages: fluent in English, German is a plus

Additional Information

www.bosch.com/research
https://www.bosch-ai.com

The final PhD topic is subject to your university.

Start: according to prior agreement

Please submit all relevant documents (incl. curriculum vitae, certificates).

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need support during your application?
Sarah Schneck (Human Resources)
+49 9352 18 8527

Need further information about the job?
Irlan Grangel Gonzalez (Functional Department)
 +49 711 811 92686