Please join us for our CCN Seminar on November 11th where our guest speaker, Michael Douglas, Theoretical Physicist and Professor, Stonybrook University, will present on " Knowledge Graph Embeddings and Inference." Abstract below!
Abstract: A knowledge graph (KG) is a data structure which represents entities and relations as the vertices and edges of a directed graph. Two examples are Wikidata for general knowledge and SemMedDB for biomedical data. A popular KG representation method is graph embedding, which facilitates question answering, inferring missing edges, and logical reasoning tasks. In this talk we introduce the topic and explain relevant mathematical results on graph embedding. We then analyze KG inference into several mechanisms: motif learning, network learning and unstructured statistical inference, and describe experiments to measure the contributions of each mechanism. Joint work with M. Simkin, O. Ben-Eliezer, T. Wu, S. P. Chin, T. V. Dang and A. Wood.
Please feel free to convene in the 7th Floor Classroom if you are onsite, and if tuning in remotely, please use the zoom credentials below.