Industry
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.
Ph.D. Information Science (ABD)
Wells Fargo
https://www.wellsfargo.com/
Alex Kalinowski is a Senior Data Scientist and Knowledge Graph Engineer at Wells Fargo. He has worked on many transformational data science projects within the bank, including the development of an open-source data science architecture, development of several internally used natural language processing libraries and model development and validation for a variety of business needs.
