Alex Kalinowski is looking to integrate knowledge graphs and natural language processing. He has a Ph.D. in information science from Drexel University. At Wells Fargo, Alex works as a knowledge graph engineer. His interests include applying deep learning techniques to graph-structured data, building tools to expedite knowledge graphs and ontology construction.
2021 Talk: Structured to Unstructured and Back: Integrated Knowledge Graphs and Natural Language Processing Techniques
It is a difficult task for traditional pattern-based matching or machine learning approaches to identify entities and the relationships they share. These techniques rapidly overfit training datasets and struggle to transfer to other contexts or domains. One solution to the lack of transferability includes the utilization of outside knowledge, such as facts contained in a knowledge base or ontology. However, integrating unstructured data such as language models with highly structured data such as knowledge bases is a challenging research problem.
Using concepts from distant supervision, word vectors, and knowledge graph embeddings, an elegant unsupervised learning approach will be presented for solving this knowledge integration problem. This talk illustrates the problem from two 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.