Enterprise Knowledge Graph for Siloed Data Integration
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Solution Overview
Problem
Enterprise knowledge is often siloed, making it difficult for auditors to leverage knowledge across different aspects of an enterprise, leading to inefficiencies and inaccuracies in audits due to the lack of portable and accessible knowledge.
Innovation Solution
The construction and use of enterprise knowledge graphs that combine conceptual, structural, and behavioral knowledge, allowing for the representation of an enterprise at multiple levels and enabling the connection and interrogation of siloed data resources, facilitating informed decision-making and risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If enterprise knowledge is stored in siloed data sources, then data storage is simple and organized, but knowledge accessibility and portability deteriorate
Solution Approach 1:
The patent merges siloed data sources into a unified enterprise knowledge graph that integrates conceptual, structural, and behavioral knowledge across multiple data sources. This combines previously separated knowledge repositories into a single accessible structure, improving knowledge accessibility while maintaining organized storage through graph-based relationships.
Solution Approach 2:
The enterprise knowledge graph serves multiple functions simultaneously: it stores data, enables knowledge retrieval, supports audit processes, and provides a portable knowledge substrate across different contexts. This multi-functional system improves accessibility without requiring separate systems for each function.
2Measurement precision
If audit knowledge is acquired in narrative form, then data collection is simple, but information loss and accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary processing system that transforms narrative audit knowledge into structured graph data. This intermediary layer converts unstructured narratives into organized conceptual, structural, and behavioral knowledge representations, improving audit accuracy while managing complexity through automated processing.
Solution Approach 2:
The system replaces manual narrative analysis with automated knowledge extraction and graph construction processes. This substitution of mechanical human analysis with computational processing improves precision while managing the complexity of knowledge processing through systematic algorithms.
3Productivity
If the same audit process is repeated without knowledge portability, then process consistency is maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The patent performs preliminary action by constructing the enterprise knowledge graph in advance, organizing conceptual, structural, and behavioral knowledge before audit processes begin. This pre-organized knowledge substrate enables efficient retrieval and application during audits, improving productivity without requiring complex real-time processing.
Solution Approach 2:
The system creates a portable copy of enterprise knowledge in the form of a knowledge graph that can be reused across different audit processes and contexts. This copying mechanism allows audit teams to leverage previously acquired knowledge without recreating it, significantly improving efficiency while maintaining process consistency.
Data Source
AI summary
A system for generating and inferencing using enterprise knowledge graphs is provided. The system receives first input data related to one or more entities from one or more data sources. The system extracts a first set of data components from the first input data and determines, based upon the extracted data components, a second set of data components. The system identifies one or more relationships between the first set of data components and the second set of data components and generate a knowledge graph comprising a plurality of nodes. A first node of the knowledge graph can represent a first respective data component of the first set of data components and a second node of the knowledge graph can represent a second respective data component of the second set of data components. The first node can be associated with the second node based on an identified relationship between the nodes.