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Beyond Context: Answering Deeper Questions by Combining Spark NLP and Graph Database Analytics

The first challenge of “ad hoc data analysis” is semantic, not technological. Data analytics users could be a patient, practitioner, administrator, or data scientist —the results don’t change based on the person asking the questions.

Lack of Interoperability between natural language and structured languages (SQL) leads to multiple interfaces, models, and views of the same data. Fortunately, the use of modern natural language processing (NLP) and graph modeling techniques minimizes such challenges.

TigerGraph, a distributed graph database, serves the purpose of semantic modeling, multi-sources integration, ad-hoc query analysis, compliance, and regulations.

Spark NLP for Healthcare – the most widely used, accurate, and scalable medical NLP library – provides linguistic, semantic, contextual, and personalized capabilities. This session describes an end-to-end solution that exceeds current BI platforms and delivers on connected analytics by exposing data patterns that combine conversational, predictive, and inference purposes.

For example, how do we go beyond “Who was the first Covid Patient?” to also answer “How will the city be impacted in the next 2 days?” Join us for a practical NLP solution that delivers state-of-the-art results with a quick implementation of Big Data in the healthcare domain.

Free & Open-Source Software from John Snow Labs: 2021 Update

We’ll share highlights and hidden gems from John Snow Labs’ recent contributions to the open-source community, and announce new free software tools...