Knowledge Graph Populator for Automated Document Retrieval
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Solution Overview
Problem
Organizations face challenges in efficiently organizing and retrieving relevant documents across various formats and storage systems, leading to time-consuming searches and inconsistent messaging due to the complexity and variability of documentation.
Innovation Solution
A method involving a graph neural network that classifies documents by recording feature embeddings as nodes in a knowledge graph and connecting them with engagement edges based on telemetry data, allowing for automated organization and retrieval of relevant documents independent of format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If documents are stored across many different formats and locations, then the document datastore contains comprehensive documentation, but it becomes difficult to find and share relevant documents
Solution Approach 1:
The system segments the unstructured document collection into structured knowledge graph components (nodes and edges), organizing documents by their semantic relationships and engagement patterns rather than physical storage locations or formats
Solution Approach 2:
The knowledge graph acts as an intermediary layer between the diverse document formats and the user search query, translating various document types into a unified graph structure that enables consistent retrieval across all formats
2Quantity of substance
If more documentation is added to the data store, then the documentation becomes more comprehensive, but searching becomes more time-consuming and produces less focused results
Solution Approach 1:
The system performs preliminary organization by automatically populating the knowledge graph with document nodes and engagement edges before search occurs, pre-computing relationships and engagement metrics so that queries can quickly retrieve relevant documents without scanning the entire collection
Solution Approach 2:
The system replaces manual search and filtering mechanisms with automated graph neural network processing, using machine learning to rapidly identify and rank relevant documents based on engagement patterns and semantic relationships
3Ease of operation
If metadata searching is used to refine document selection, then some filtering is achieved, but it does not provide enough focus as more documentation is added
Solution Approach 1:
The system transitions from simple metadata parameters to complex engagement-based parameters (engagement telemetry data, interaction patterns, user behavior metrics) that provide much finer granularity for distinguishing relevant from irrelevant documents
Solution Approach 2:
The system combines multiple data sources (document metadata, engagement telemetry, user interactions, document relationships) into a composite knowledge graph structure that provides multi-dimensional filtering capabilities far exceeding single-parameter metadata search
Data Source
AI summary
A computing device records feature embeddings of each document as a document node of a knowledge graph and connects each document node of the knowledge graph with one or more engagement edges based on engagement telemetry data indicating a measure of engagement with the documents stored in the document datastore. The computing device trains a graph neural network using the knowledge graph populated with each document node and the one or more engagement edges. The computing device may generate a feature embedding for the document query and classify one or more documents from the document datastore as relevant to the document query using the graph neural network based on the feature embedding of the document query.