MC Miner™ is an integrated, cross talking, multi-component solution for content ingestion, enrichment and exchange including;
- A parser to ingest various forms of inputs (structured and unstructured)
- Entity Recognition module to Identify different categories of named entities
- Ontologies/CVs of named entities (Authors, Topics, Institutions, Places etc., With standardizations)
- An AI based clustering algorithm for automated clustering of similar named entities
- ML rule based engine to influence the said clustering via plug and play user inputs
- Optional component allowing for manual intervention to disambiguate semi-automated data points
- Entity centric recommendations and similarity suggestions on the content
- APIs to the access the linked data store consisting of the disambiguated and standardized entities from all/any of the above workflow components
Key Features :
Context aware and entity centric summarizations. For instance, this engine can address use cases like identifying content specifically centered around evidence based treatments for a specific disease. Create building blocks for a semantic data lake and knowledge discovery solutions Data repurposing like collection creations Content recommendations tailored for user engagement, discovery led content consumptions etc.,