Semantic Graph Database for CRM Inference Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current CRM systems lack the infrastructure to effectively utilize the vast amount of data they collect, leading to inefficient inference processes due to the duplication of simple inference rules across large knowledge bases, resulting in high computational costs.
Innovation Solution
Implementing a semantic graph database with a triple server and voice server architecture that parses and infers CRM events into semantic triples, using inference engines to connect data points efficiently and reduce the burden on the system by storing and processing data in a structured format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inference rules are run against a complete data set to determine additional relationships among entities stored in a huge knowledge graph, then the ability to infer relationships (e.g., sibling relationships) is improved, but the computational cost becomes very expensive due to the massive size of the knowledge base
Solution Approach 1:
The patent segments the massive knowledge graph into smaller subgraphs based on entity types and relationships. Instead of running inference rules against the complete knowledge graph, the system divides it into manageable portions that can be processed independently, reducing computational overhead while maintaining inference accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing inferred relationships in the knowledge graph before they are needed. Inference rules are applied in advance to populate the graph with derived relationships, so that when queries are executed, the results are already available without requiring costly real-time computation.
2Quantity of substance
If CRM systems store vast amounts of data from multiple communication channels, then the quantity of information available for analysis is improved, but the infrastructure capability to effectively utilize this data deteriorates
Solution Approach 1:
The patent introduces a semantic graph database as an intermediary layer between the raw CRM data and the analysis tools. This intermediary structure organizes unstructured and semi-structured data from multiple communication channels into a standardized triple store format, making the data more accessible and easier to query without requiring complex infrastructure changes.
Solution Approach 2:
The system changes the parameter of data representation by converting diverse data formats (emails, chat logs, call records) into a unified semantic triple structure. This parameter change allows different data types to be processed uniformly, reducing infrastructure complexity while maintaining the ability to handle vast quantities of multi-channel data.
3Measurement precision
If simple inference rules are duplicated billions of times across a large knowledge base, then the coverage of inferred relationships is improved, but the processing time and computational resources required deteriorate
Solution Approach 1:
Instead of duplicating inference rules billions of times across the knowledge base, the system creates a single instance of each inference rule and applies it to relevant subgraphs. The results are then propagated or copied to the appropriate locations in the knowledge graph, dramatically reducing processing time while maintaining comprehensive inference coverage.
Solution Approach 2:
The system performs inference operations in advance on subgraphs before they are needed for querying. By pre-computing inferred relationships and storing them in the knowledge graph, the system avoids the need to re-execute inference rules repeatedly during query operations, significantly reducing processing time while maintaining full inference coverage.
Data Source
AI summary
Customer relationship management (“CRM”) implemented in a computer system, including parsing, by a parsing engine of the computer system into parsed triples of a description logic, words of a CRM event from an incoming stream of CRM events, the CRM event characterized by an event type, the stream implemented in a CRM application of the computer system; and inferring, by an inference engine from the parsed triples according to inference rules specific to the event type, inferred triples.