Graph-Based Clinical Knowledge Indexing via Preliminary Flattening
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Solution Overview
Problem
Existing clinical knowledge bases are limited in the types of queries they can support, hindering efficient data access and decision-making in medical services.
Innovation Solution
A method is introduced where data is represented as a graph, with assertions as reified n-ary relations, allowing for traversal and conversion into a searchable document index, enabling efficient querying and retrieval of clinical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is represented as a graph with assertions as reified n-ary relations, then the system can support more complex query types and improve data retrieval efficiency, but the system complexity increases due to graph traversal and index conversion processes
Solution Approach 1:
The system performs preliminary graph traversal and flattening operations during the indexing phase to convert the graph structure into a searchable document index. This pre-processing creates a simplified representation that can be efficiently queried without repeatedly traversing the complex graph structure, thus supporting versatile queries while managing system complexity through advance preparation.
Solution Approach 2:
The patent introduces a document index as an intermediary structure between the graph data representation and the query processing mechanism. This index serves as a mediator that translates complex graph queries into efficient search operations, enabling versatile query support while abstracting away the graph traversal complexity from the query processing layer.
2Productivity
If the graph is traversed and flattened into a document index, then data retrieval efficiency is improved, but processing time and computational resources are consumed during the conversion process
Solution Approach 1:
The system performs graph traversal and flattening operations during the indexing phase as a preliminary action. By pre-converting the graph structure into a searchable document index, the system enables fast query execution without repeatedly traversing the graph, thus improving data retrieval efficiency while concentrating computational resources during the initial indexing process rather than during query processing.
3Adaptability or versatility
If assertions are represented as reified n-ary relations in the graph, then the system can model complex relationships, but the graph structure becomes more complex and harder to traverse
Solution Approach 1:
The system performs preliminary graph traversal during the indexing phase to convert the complex graph structure with reified n-ary relations into a flattened document index. This pre-processing preserves the complex relationship modeling capability while creating a simplified structure that is easier to traverse and query, thus resolving the contradiction between relationship modeling capability and graph traversal complexity.
Solution Approach 2:
The document index serves as an intermediary that bridges the complex graph structure with simpler query processing. It preserves the rich relationship information from the reified n-ary relations while providing a simplified access pattern that reduces traversal complexity during query execution.
Data Source
AI summary
Ranking of data and retrieval of data or relationships of the data responsive to the ranking. A data model is represented as a graph, with edges in the graph linking related concepts, and an assertion represented in the graph as a reified n-ary relation. The graph is ingested though traversal and storage of the node paths. The traversal includes concepts and categories. In addition, neighborhood of one or more adjacent concepts are followed and stored in relation to the node paths. The ingested graph is converted into a set of flat document structures supported by information ranking and a retrieval framework.