Knowledge Acquisition & Interferencing
To extract knowledge from multiple resources including structured and unstructured, represent it in the form of Ripple Down Rules (RDR) which is easy to use and evolvable with minimum efforts.
The primary feature of knowledge extraction include extracting knowledge from divers and convert that into actionable/executable knowledge. To acheive this, we go through some of the other features described as follows.
1. Accept structured data (EMR, EHR) and unstrctured data (text, images) as input for knowledge extraction.
2. To extract knowledge from textual resources, machine learning and NLP techniques perform to generate n-grams, named entities (concepts), concept hierarchy (concept tree) and attches them to the existing domain ontology.
3. The main features of knowledge extraction from medical images includes category classification, segmentation, local feature extraction and model collection.
4. The knowledge extracted from textual resources in the form of ontology and form visual resources are utilized by Actionable knowledge components to transform the extracted knowledge to actionable / executable form.
5. Actional knowledge is stored in the form of RDR to verify and evolve with minim efforts and with intervention of knowledge engineers.
Uniquesness & Contributions
1. A novel approach of evolutionary knowledge acqusition and modeling with real-time verification.
2. Automated ontology construction and evolution.
3. Enhancing image quality with noise reduction and contrst enhancement for knowledge extraction.
4. Generate specific model for particular image type and organ types