Functionally, the platform is based on Working Systems, designed by level. The Working Systems acts as a digital data factory that powers the Exorde Knowledge Graph, or Exorde Graph:

1) Working Systems: the first level of the system, where participants index various URLs taken from the Internet and their relationships to the data (similarities, correlations) according to predefined rules and guidelines. This set of rules is established and approved by the Exorde community and can evolve to increase relevance and maximize the value of the data.

2) Data Analysis System: the second level of the system, where participants perform continuous analysis of different types of data on the linked and indexed data that make up the main Exorde database (knowledge graph). It includes data clustering, trend analysis, labeling, tracing, and partitioning, and is performed using sentence-encoding (or document-encoding) NLP models such as BERT (or other transformer-based deep learning models). Text objects (sentences, paragraphs, headings, etc.) are transformed into numerical vectors and then added to the underlying neural database, allowing for scaling indexing and queries using Exorde Contributors. These data operations will increase in number and variety over time to match demand and maximize the relevance of Exorde services and products.

#Exorde #ExordeLabs $EXD #web3 #protocol

Functionally, the platform is based on Working Systems, designed by level. The Working Systems acts as a digital data factory that powers the Exorde Knowledge Graph, or Exorde Graph:

1) Working Systems: the first level of the system, where participants index various URLs taken from the Internet and their relationships to the data (similarities, correlations) according to predefined rules and guidelines. This set of rules is established and approved by the Exorde community and can evolve to increase relevance and maximize the value of the data.

2) Data Analysis System: the second level of the system, where participants perform continuous analysis of different types of data on the linked and indexed data that make up the main Exorde database (knowledge graph). It includes data clustering, trend analysis, labeling, tracing, and partitioning, and is performed using sentence-encoding (or document-encoding) NLP models such as BERT (or other transformer-based deep learning models). Text objects (sentences, paragraphs, headings, etc.) are transformed into numerical vectors and then added to the underlying neural database, allowing for scaling indexing and queries using Exorde Contributors. These data operations will increase in number and variety over time to match demand and maximize the relevance of Exorde services and products.

#Exorde #ExordeLabs $EXD #web3 #protocol