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Graph Analytics

Knowledge Representation and Reasoning

PNNL develops intelligent systems that can learn from raw data and answer complex queries. Our technical focus is twofold: 1) scalable learning of semantic graphs or continuous feature representation of graph models and 2) algorithm development for end-user problems, such as event detection, relation classification, or explaining observations via inferencing. This research impacts applications for natural language processing, cyber security, and health informatics. A key aspect of the work involves integrating graph algorithms and deep learning by building on frameworks, such as Apache Spark and TensorFlow.



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PNNL