Many interesting applications require data from different sources: relational databases, the web, text documents, ... Combining these data in an intelligent and flexible way is often a big challenge. If it can be achieved, it allows powerful tools to be developed that provide sophisticated decision support in a variety of domains.

In this project, we aim to develop a methodology for combining data from different sources to solve complex configuration problems in a decentralised way, i.e., without physically merging the data. We base our methodology on:

• Knowledge Graphs and other Semantic Technology for data integration
• Machine Learning to automatically link different data sources together
• Knowledge-Base System technology to turn data into solutions

We use a combination of state-of-the-art technology developed at KU Leuven and industry-standard solutions.

This project aims to help companies solve concrete data integration and configuration problems in a state-of-the-art way. To this end, we will:
• Develop real-life case studies
• Perform objective comparisons and evaluations of the available tools
• Provide training, support and guidelines for making effective use of these tools