In this Building with Watson webinar, Watson Knowledge Studio Offering Manager Stefan Tzanev and Watson and Cloud Adoption Leader Randy Haven outline the latest updates in Knowledge Studio, showing you how they enable domain experts to combine machine learning with a rule-based approach so you can accelerate both the development and accuracy of your learning model. During this session, you will learn how to build rule-based models and how to use them to pre-annotate machine learning models.

Knowledge Studio is a cloud-based application that enables developers and domain experts to collaborate and create custom annotator components for unique industries. These annotators can identify mentions and relationships in unstructured data and be easily administered throughout the lifecycle using one common tool. Annotator components can be deployed directly to IBM Watson Explorer, Watson Natural Language Understanding, and Watson Discovery services.

You can use Knowledge Studio to create a machine-learning model that understands the linguistic nuances, meaning, and relationships specific to your industry or to create a rule-based model that finds entities in documents based on rules that you define. Knowledge Studio provides easy-to-use tools for annotating unstructured domain literature and uses those annotations to create a custom machine-learning model that understands the language of the domain.

The accuracy of your model improves through iterative testing, so your ultimate result is an algorithm that can learn from the patterns that it sees and recognize those patterns in large collections of new documents.

You can deploy the finished machine-learning model to other cloud-based offerings and cognitive solutions to find and extract mentions of relations and entities, including entity coreferences.

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