This presentation describes how to build tools to find the meaning of unstructured text using machine generated knowledge representation graphs using NLP and ai-one’s Topic-Mapper API.
The prototype solution, called ai-Browser, is a generalized approach that can solve the following types of use cases:
- Sentiment analysis of social media feeds
- Evaluating electronic medical records for clinical decision support systems
- Comparing news feeds
- Electronic discovery for legal purposes
- Automatically tagging documents
- Building intelligent search agents
The source code for ai-Browser is available to developers to customize to meet specific requirements. For example:
- Healthcare providers can use ai-Browser to analyze medical records by using ontologies and medical lexicons.
- Social media marketing agencies can use ai-Browser to create personal profiles of customers by reading social media feeds.
- Researchers can use ai-Browser to mine PubMed and other repositories.
Our goal is to get the source code and the API into the hands of commercial companies who want to tailor the application to solve specific problems.
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