Graphical Structure Document Retrieval Using Topic Annotations
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Solution Overview
Problem
Existing document retrieval and recommendation systems face challenges in scalability and relevance, often trading off relevance for the ability to handle large-scale systems effectively, resulting in non-relevant search results.
Innovation Solution
A graphical structure is used to represent people and documents, with nodes connected by edges based on observed interactions, and annotated with topics, allowing for efficient and robust document retrieval and recommendation through a node relatedness calculator and retriever system that computes distances and recommends relevant documents and individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional document retrieval systems are used to handle large-scale document repositories, then the system can scale to process huge numbers of documents, but the relevance of retrieved documents deteriorates
Solution Approach 1:
The patent introduces topic annotations as an intermediary layer between documents and users. These topics serve as mediators that capture the semantic meaning of documents, enabling the system to retrieve relevant documents at scale by matching user queries with topic annotations rather than requiring direct analysis of all documents in the repository
Solution Approach 2:
The patent replaces traditional mechanical search mechanisms (keyword matching, full-text search) with a topic-based semantic representation system. By substituting the mechanical retrieval process with topic annotation and matching, the system achieves both scalability and relevance
2Measurement precision
If more data is collected to improve recommendation accuracy, then the relevance of recommendations improves, but the system becomes less robust to data limitations and errors
Solution Approach 1:
The patent changes the parameter representation from raw interaction data to topic annotations. By transforming the data representation, the system becomes less sensitive to data quality issues and more robust to limitations in the available data while maintaining recommendation accuracy
3Quantity of substance
If automated topic calculation is used instead of manual annotation, then the system can scale to process large numbers of documents, but the precision of topic annotations may deteriorate
Solution Approach 1:
The patent implements self-service through automated topic calculation where the system generates its own topic annotations without human intervention. The automated process analyzes document content and generates topic tags, enabling the system to scale to large document repositories while maintaining acceptable annotation quality through algorithmic precision
Data Source
AI summary
A system for retrieving/identifying a document comprising text stored in a document repository is described. A memory stores a graphical structure comprising a first plurality of nodes each representing a person, and a second plurality of nodes each representing a document in the document repository, the nodes being connected by edges according to automatically observed interactions between the represented people and documents. At least some of the nodes have one or more annotations each denoting a topic. A node relatedness calculator computes distances between nodes of the graphical structure using the topic annotations. An input receives an identifier of a user who is represented by one of the first plurality of nodes. An identifier/retriever identifies one or more documents from the document repository by using the identifier and using the computed distances between nodes.