Indirect Connection Clusters for Non-Obvious Topic Discovery
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Solution Overview
Problem
Users face difficulties in generating content on complex topics when starting with a blank document, as they lack relevant information and may not conduct extensive research, leading to limited search results from traditional search engines.
Innovation Solution
The system utilizes an information graph to identify indirect connection clusters (ICCs) between articles, suggesting non-obvious topics by analyzing the geometry of connections within the graph, allowing for the discovery of hidden relationships and providing more comprehensive search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional search engines are used to find information on complex topics, then search results are limited to directly connected terms, but users cannot discover hidden relationships or non-obvious topics without extensive research
Solution Approach 1:
The system pre-computes and stores indirect connection clusters (ICCs) in the information graph before user queries are submitted. By performing the complex graph traversal and cluster identification in advance, the system makes hidden relationships immediately available without requiring users to conduct extensive real-time research
Solution Approach 2:
The patent introduces an intermediary structure called the information graph that contains pre-identified ICCs. This intermediary stores the results of complex relationship analysis, allowing users to access indirect connections through simple queries rather than performing extensive research themselves
2Ease of operation
If users start with a blank document and lack prior knowledge, then they cannot generate sufficient search terms, but traditional search requires significant prior knowledge to provide sufficient search terms
Solution Approach 1:
The system pre-identifies and stores ICCs in the information graph before users begin their research. When users submit even basic search terms, the system can immediately return comprehensive results including non-obvious topics that would otherwise require extensive prior knowledge to discover
Solution Approach 2:
The information graph automatically identifies and presents relevant ICCs based on user queries, eliminating the need for users to manually navigate through extensive research or possess deep domain knowledge. The system serves itself by pre-computing relationships and making them accessible through simple interfaces
3Adaptability or versatility
If relevant information is provided based on document context and search terms, then results are limited to direct connections, but users desire wider range and deeper understanding of related topics
Solution Approach 1:
The system performs complex ICC identification and stores results in the information graph in advance. This pre-computation allows the system to provide wide-ranging topic suggestions without performing complex analysis in real-time, thus maintaining versatility while managing computational complexity
Solution Approach 2:
The patent introduces an intermediary information graph structure that stores pre-analyzed ICCs. This intermediary handles the complexity of relationship analysis separately, allowing the user interface to remain simple while providing comprehensive, adaptable topic suggestions
Data Source
AI summary
By applying a set of simple geometric rules to the connections within a connected graph of ‘topics’ it is possible to uncover hidden relationships that are otherwise inaccessible to the lay person. Interesting, potentially non-obvious threads of content, termed indirect connection clusters (ICCs), can be found from an online encyclopedia or other graph of articles that are not directly connected to a starting topic, but instead are connected via an intermediate clique of articles. A system performing a search of an information graph can receive a request for identifying relevant content, identify (in a traversal of the information graph) one or more ICCs using a starting topic associated with the request, refine the one or more ICCs (eliminating certain ICCs) and generate a refined set of ICCs, and rank ICCs within the refined set. The ranked ICCs can be provided in response to the request for identifying relevant content.