Integrating Language Models and Knowledge Graphs for Enterprise Data Management

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. The purpose of this talk is to view the problem from both points-of-view: the natural language processing practitioner unaccustomed to semantics and knowledge bases, and the semantic web developer without a background in deep learning and language models.