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2017-12-11 |
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This documentation is for {{site.data.keyword.knowledgestudiofull}} on {{site.data.keyword.cloud}}. To see the documentation for the previous version of {{site.data.keyword.knowledgestudioshort}} on {{site.data.keyword.IBM_notm}} Marketplace, click this link 
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Use {{site.data.keyword.knowledgestudiofull}} 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. {: shortdesc}
To become a subject matter expert in a given industry or domain, {{site.data.keyword.watson}} must be trained. You can facilitate the task of training {{site.data.keyword.watson}} with {{site.data.keyword.watson}}™ {{site.data.keyword.knowledgestudioshort}}.
{{site.data.keyword.watson}} {{site.data.keyword.knowledgestudioshort}} 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 the model improves through iterative testing, ultimately resulting in 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 {{site.data.keyword.watson}} cloud-based offerings and cognitive solutions to find and extract mentions of relations and entities, including entity coreferences.
The following diagram illustrates how it works.
- Based on a set of domain-specific source documents, the team creates a type system that defines entity types and relation types for the information of interest to the application that will use the model.
- A group of two or more human annotators annotates a small set of source documents to label words that represent entity types, to identify relation types where the text identifies relationships between entity mentions, and to define coreferences, which identify different mentions that refer to the same thing, that is, the same entity. Any inconsistencies in annotation are resolved, and one set of optimally annotated documents is built, which forms the ground truth.
- {{site.data.keyword.watson}} {{site.data.keyword.knowledgestudioshort}} uses the ground truth to train a model.
- The trained model is used to find entities, relations, and coreferences in new, never-seen-before documents.
See Creating a machine-learning annotator for more details.
{{site.data.keyword.watson}} {{site.data.keyword.knowledgestudioshort}} provides a Rules editor that simplifies the process of finding and capturing common patterns in your documents as rules. You can then create a model that recognizes the rule patterns, and deploy it for use in other services.
See Creating a rule-based annotator for more details.
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Share domain artifacts and models between {{site.data.keyword.knowledgestudiofull}} and other {{site.data.keyword.watson}} services.
Use {{site.data.keyword.watson}} {{site.data.keyword.knowledgestudioshort}} to perform the following tasks:
- Bootstrap annotation by using the {{site.data.keyword.alchemylanguageshort}} entity extraction service to automatically find and annotate entities in your documents. When human annotators begin to annotate the documents, they can see the annotations that were already made by the service and can review and add to them. See Pre-annotating documents with {{site.data.keyword.IBM_notm}} {{site.data.keyword.alchemylanguageshort}} for details.
- Import analyzed documents that are in UIMA CAS XMI format. For example, you can import UIMA CAS XMI files that were exported from {{site.data.keyword.IBM_notm}} {{site.data.keyword.watson}} Explorer content analytics collections or {{site.data.keyword.IBM_notm}} {{site.data.keyword.watson}} Explorer Content Analytics Studio.
- Deploy a machine-learning or rule-based model to use with the {{site.data.keyword.watson}} {{site.data.keyword.discoveryshort}} service.
- Deploy a machine-learning or rule-based model to use with the {{site.data.keyword.nlushort}} service.
- Export a machine-learning model to use in {{site.data.keyword.IBM_notm}} {{site.data.keyword.watson}} Explorer.
- Export a rule annotator PEAR file to use in {{site.data.keyword.IBM_notm}} {{site.data.keyword.watson}} Explorer.