Business Intelligence for the Circular Economy — Constructing a Domain-Specific Knowledge Graph
Teaching
Motivation and Task
Companies are increasingly required to meet government regulations and customer demands pertaining to sustainable production and sustainable product offerings. As a result, companies must find, adopt, and develop new methods of production and consumption to keep their license to operate. One emerging knowledge field is the “circular economy”, a broad model of production and consumption that involves sharing, leasing, reusing, repairing, refurbishing and recycling existing materials and products for as long as possible.
Within this field, numerous solutions are already being proposed and described in theory or as best practices adopted and implemented by companies. The challenge is that this knowledge is dispersed over many sources, unstructured, heterogeneous in format and detail, and therefore very difficult to search systematically for practitioners looking for suitable solutions or at least inspirations.
The goal of this thesis is to develop and implement an approach that makes the knowledge field “circular economy” more accessible and searchable by deploying tools from natural language processing and machine learning.
Possible Subtasks (not all required for one thesis)
- Extracting text data on the topic of circular economy:
- Dedicated websites / best practice databases
- Scientific articles
- (Social) media & press articles
- Company reports
- Concepts / keyphrase extraction
- Named entities (firms, industries) extraction
- Constructing an ontology / knowledge graph
- Generating knowledge graph embeddings
- Knowledge graph analysis and visualization
- Implement keyword searches over the knowledge graph
Expectations
- Master students with an interest in circular economy
- Prior experience in Python / R
- Prior experience with or strong motivation to learn about NLP
- Thesis can be written in German or English
- Thesis can be assigned to several students dealing with different data sources