Knowledge Graph Orchestration for Data Silo Resolution
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Solution Overview
Problem
Existing data storage systems in enterprises face inefficiencies in extracting, transforming, and loading unstructured data, leading to inaccurate responses to information queries and excessive resource allocation, as they lack a structured approach to relationship data, resulting in 'data dumps' that are not contextually useful.
Innovation Solution
A knowledge graph system that structures data into a graph presentation capturing entities, relationships, and attributes with semantic meaning, utilizing a graph schema definition, data processing pipelines, and orchestration processes to integrate and refine data from multiple sources, enabling more accurate and relevant information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If string matching mechanisms are used to search unstructured data, then information retrieval is enabled, but accuracy of responses deteriorates and computing resources are excessively allocated
Solution Approach 1:
The patent segments unstructured data into structured knowledge graphs with defined entities, relationships, and attributes. This segmentation transforms the monolithic unstructured data into organized components that can be efficiently queried, improving both accuracy and resource utilization by eliminating the need to process entire unstructured datasets for simple queries.
Solution Approach 2:
The knowledge graph acts as an intermediary layer between unstructured data storage and query operations. By pre-structuring data into knowledge graphs with defined schemas, the system mediates between raw unstructured data and search queries, enabling accurate responses without directly processing large volumes of unstructured data during query execution.
2Ease of operation
If unstructured data is extracted and transformed for searching, then information accessibility is improved, but data accuracy and contextual usefulness deteriorate
Solution Approach 1:
The system performs preliminary structuring of unstructured data into knowledge graphs before querying occurs. By pre-defining schemas, entities, relationships, and attributes during data ingestion, the system prepares data in advance for accurate retrieval without losing contextual information during the query process.
Solution Approach 2:
The patent changes the structural parameters of data from unstructured formats to structured knowledge graph formats with defined schemas. This parameter transformation includes establishing entity types, relationship types, and attribute definitions, which preserve contextual usefulness while enabling efficient access.
3Ease of manufacture
If data is stored in data lakes or warehouses without relationship structure, then data aggregation is simplified, but data utility and query performance deteriorate
Solution Approach 1:
The patent segments aggregated data into structured knowledge graphs with hierarchical relationships. By organizing data into entities, relationships, and attributes with defined schemas, the system maintains the aggregation benefit while adding structural organization that enables efficient querying and analysis.
Solution Approach 2:
The system adds a structural dimension to aggregated data by organizing it into knowledge graphs with multiple layers (entities, relationships, attributes). This dimensional transformation from flat aggregation to hierarchical structure enables both simplified data collection and high-performance querying.
Data Source
AI summary
A knowledge data management system (KDMS) implements data refinement orchestration, resolution, and refinement to provide a reusable and generic solution to ingest and link data via relationships and properties when constructing a knowledge graph. The KDMS thus operates to break down existing data storage silos by normalizing and integrating data with a uniform semantic schema, which results in more accurate and faster knowledge graph construction.