Graph Data Structure for Web Topic Relationship Identification
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Solution Overview
Problem
The World Wide Web contains vast, unstructured knowledge scattered across diverse web pages with no formal relation, making it difficult to extract reliably and efficiently.
Innovation Solution
A technique that uses a machine-trained model to identify topics in web documents, generating count and conditional probability information to create a graph data structure representing hierarchical relationships among topics, enabling efficient discovery and utilization of latent information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex semantic analysis and relationship modeling are applied to web documents, then the accuracy of topic relationship identification improves, but the computational resources and processing time increase significantly
Solution Approach 1:
The patent segments the knowledge extraction process into two distinct stages: (1) topic identification using a machine-trained model, and (2) relationship discovery using conditional probability analysis. This segmentation allows each stage to use the most appropriate method for its specific task, avoiding the computational overhead of applying complex relationship modeling to every document while still achieving accurate topic relationship identification through the systematic two-stage approach.
Solution Approach 2:
The patent introduces conditional probability analysis as an intermediary mechanism between raw document data and final relationship models. Instead of directly applying complex semantic analysis to web documents, the system uses conditional probability as a mediator to infer relationships from topic co-occurrence patterns, significantly reducing computational requirements while maintaining relationship identification accuracy.
2Loss of information
If comprehensive topic relationship modeling is performed on all web documents, then the completeness of knowledge extraction improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by first identifying topics in web documents using a pre-trained machine learning model before performing relationship analysis. This preliminary topic identification step filters and structures the unstructured web document data, enabling subsequent conditional probability analysis to efficiently discover relationships without requiring comprehensive analysis of every document from scratch, thus reducing processing time while maintaining knowledge extraction completeness.
Solution Approach 2:
The patent uses partial action by focusing computational resources on analyzing topic co-occurrences rather than performing full semantic analysis on entire documents. The conditional probability analysis examines only the relationship patterns between identified topics across documents, which is a partial but sufficient approach for discovering hierarchical relationships without the excessive time cost of comprehensive document-level semantic modeling.
3Measurement precision
If manual evaluation and complex model development are required, then the accuracy of topic identification improves, but the scalability and ease of deployment deteriorate
Solution Approach 1:
The patent implements self-service by using a machine-trained model that automatically identifies topics in web documents without requiring manual evaluation or expert annotation. The system trains on available data and independently performs topic identification and relationship discovery, eliminating the need for continuous human intervention while maintaining accurate topic identification. This automation enables the system to scale to large numbers of documents without proportionally increasing manual labor requirements.
Solution Approach 2:
The patent leverages parameter changes by using a machine-trained model with learned parameters that can adapt to different domains and document types. The model's parameters are trained on representative data and can be adjusted or retrained as needed, providing both accurate topic identification and scalability. The conditional probability calculations also use configurable threshold parameters that can be tuned to balance precision and recall based on specific application requirements.
Data Source
AI summary
A technique produces a graph data structure based on at least partially unstructured information dispersed over web documents. The technique involves applying a machine-trained model to a set of documents (or, more generally “document units”) to identify topics in the documents. The technique then generates count information by counting the occurrences of the single topics and co-occurrences of parings of topics in the documents. The technique generates conditional probability information based on the count information. An instance of conditional probability information describes a probability that a first topic will occur, given an appearance of a second topic, and a probability that the second topic will occur, given an appearance of the first topic. The technique then formulates the conditional probability information in a graph data structure. The technique also provides an application system that utilizes the graph data structure to provide any kind of computer-implemented service to a user.