Semantic Knowledge Graph for Organizational Memory
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Solution Overview
Problem
Business intelligence systems face challenges in providing ease of use and insights, as they struggle to effectively parse and contextualize data from various interactions with data sources, leading to inefficiencies in data storage and analysis.
Innovation Solution
The method involves generating a semantic knowledge graph by parsing events into objects, determining relationships between them, and creating a graph with query nodes and edges, allowing for the representation of complex data interactions and user-specific insights without direct access to the data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If business intelligence systems directly access and store raw data from multiple data sources, then data completeness and accuracy are improved, but system complexity and storage requirements increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (semantic knowledge graph) between raw data sources and the business intelligence system. This intermediary parses events into objects, determines relationships between them, and generates a structured knowledge representation that simplifies downstream processing while preserving data completeness and accuracy.
2Loss of information
If business intelligence systems store and process all raw events and interactions, then analysis depth and insights are improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing events into objects and determining relationships between them before actual analysis queries are executed. The semantic knowledge graph is generated in advance, organizing data into a structured format with defined relationships, which significantly accelerates subsequent analysis operations while preserving analytical depth.
3Adaptability or versatility
If business intelligence systems maintain detailed records of all user interactions and queries, then organizational memory and customization capabilities are improved, but data storage requirements and system overhead increase
Solution Approach 1:
The patent extracts essential information from detailed user interactions and queries, identifying key objects and relationships rather than storing complete raw event data. The semantic knowledge graph captures the essential structure and relationships needed for customization and organizational memory while eliminating redundant detailed records, reducing storage requirements while maintaining adaptability.
Data Source
AI summary
A system and method for generating a semantic graph. The method includes: parsing each of a plurality of events into a plurality of objects, wherein the plurality of events includes a plurality of queries, wherein each event of the plurality of events is related to an interaction with at least one data source; determining, for each of the plurality of events, a relationship between two objects of the plurality of objects; and generating a semantic knowledge graph based on the determined relationships, the semantic knowledge graph including a plurality of query nodes and a plurality of edges, wherein each query node corresponds to a respective object of the plurality of objects, wherein each query node is connected to another query node of the plurality of query nodes by one of the plurality of edges, wherein each edge represents a relationship between the objects connected by the edge.