Real world, business-focused success stories of how knowledge graphs, ontologies, and AI have been applied to address business needs. The track will focus on return on investment and value to organizations.
Identification of entities and the relations between them is a difficult task for traditional pattern-based matching or machine learning approaches; these techniques rapidly overfit training datasets and struggle to transfer to other contexts or domains. Utilizing outside knowledge, such as facts contained in a knowledge base or ontology, seems to be a solution to the lack of transferability. However, integrating unstructured text data and language models with highly structured resources such as knowledge bases is a challenging research problem.
Knowledge graphs are way more than just a catalog and integration layer for the hundreds or thousands of corporate data silos. We argue that it's the foundation for turning pure data into actionable knowledge that supports the right decisions at the right time. In this talk, we will share success stories of business users that are leveraging the eccenca knowledge graph technology to cut through their complexity as well as manage rules and constraints in explicit models. We will show how we enabled them to automate highly complex environments with minimal need for IT intervention.